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Wireless Body Area Networks (WBANs) play a crucial role in healthcare and various other domains by enabling continuous monitoring of vital signs and activities. However, challenges such as low-powered sensors, unstable links, and routing issues hinder their efficiency and reliability. In this paper, we propose the Thermal Aware High Throughput (TAHT) routing protocol to address these challenges. TAHT utilizes a multi-hop topology and prioritizes stable links for efficient routing, considering parameters like energy, link quality, and temperature. Through experimental evaluation, TAHT demonstrates a 10% to 20% improvement in throughput, a 15% reduction in temperature overheads, and enhanced overall performance compared to benchmark protocols. By effectively managing energy consumption, minimizing duplicate data transmissions, and optimizing routing paths, TAHT offers a novel solution to improve the reliability and sustainability of WBANs, thus advancing the field beyond previous efforts in the literature.

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resolving many problems related to the classification of fetal organs. Processing fetal ultrasound (US) images increasingly uses deep learning (DL) techniques. This paper aims to assess the development of existing DL classification systems for use in a real maternal-fetal healthcare setting. This experimental process has employed two publicly available datasets, such as FPSU23 Dataset and Fetal Imaging. Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. Following the training process, deep features were extracted from the average pooling layers of both models. In a subsequent step, the features from both models were optimized using an improved version of the Generalized Normal Distribution Optimizer (GNDO). Finally, neural networks are used to classify the fused optimized features of both models, which were first combined using a new fusion technique. The best classification scores, 98.5 and 88.6% accuracy, were obtained after multiple steps of analysis. Additionally, a comparison with existing state-of-the-art methods revealed a notable improvement in the suggested architecture’s accuracy.

Fruits and vegetables are among the most nutrient-dense cash crops worldwide. Diagnosing diseases in fruits and vegetables is a key challenge in maintaining agricultural products. Due to the similarity in disease colour, texture, and shape, it is difficult to recognize manually. Also, this process is time-consuming and requires an expert person. We proposed a novel deep learning and optimization framework for apple and cucumber leaf disease classification to consider the above challenges. In the proposed framework, a hybrid contrast enhancement technique is proposed based on the Bi-LSTM and Haze reduction to highlight the diseased part in the image. After that, two custom models named Bottleneck Residual with Self-Attention (BRwSA) and Inverted Bottleneck Residual with Self-Attention (IBRwSA) are proposed and trained on the selected datasets. After the training, testing images are employed, and deep features are extracted from the self-attention layer. Deep extracted features are fused using a concatenation approach that is further optimized in the next step using an improved human learning optimization algorithm. The purpose of this algorithm was to improve the classification accuracy and reduce the testing time. The selected features are finally classified using a shallow wide neural network (SWNN) classifier. In addition to that, both trained models are interpreted using an explainable AI technique such as LIME. Based on this approach, it is easy to interpret the inside strength of both models for apple and cucumber leaf disease classification and identification. A detailed experimental process was conducted on both datasets, Apple and Cucumber. On both datasets, the proposed framework obtained an accuracy of 94.8% and 94.9%, respectively. A comparison was also conducted using a few state-of-the-art techniques, and the proposed framework showed improved performance.

Background Depression and anxiety are prevalent mental health issues addressed by online cognitive behavioral therapy (CBT) via mobile applications. This study introduces Sokoon, a gamified CBT app tailored for Arabic individuals, focusing on alleviating depression and anxiety symptoms (DASDs). Objectives The objectives of this study were to: Evaluate the effectiveness of Sokoon in reducing symptoms of depression and anxiety. Assess the usability of the intervention through user engagement and adherence to CBT skills. Methods A single-group pre–post design evaluated Sokoon's impact on adults with DASDs. In consultation with psychiatrists, Sokoon integrates evidence-based skills such as relaxation, gratitude, behavioral activation, and cognitive restructuring, represented by planets. Its design incorporates Hexad theory and gamification, supported by a dynamic difficulty adjustment algorithm. The study involves 30 participants aged 18–35 (86.7% female), specifically those with mild to moderate depression and anxiety. Results Based on a sample of 30 participants, Sokoon, a smartphone-based intervention, significantly reduced symptoms of depression and anxiety (d = 2.7, d = 3.6, p

Accurate sleep posture monitoring plays a pivotal role in the diagnosis and treatment of a spectrum of sleep-related disorders, such as sleep apnea, restless leg syndrome, and rapid eye movement sleep behavior disorder. These disorders can significantly affect an individual’s overall well-being and quality of life. However, the complexity of the human body and various factors affecting sleep patterns pose substantial challenges. Thus, this study introduces a sleep position determination approach that utilizes both RGB and thermal cameras to acquire visual and thermal data. This dual-source data acquisition system enables a comprehensive analysis of body positioning during sleep, including potential discomfort assessment. The methodology for this endeavor was initiated by acquiring a dataset encompassing various sleep postures, which was achieved through the deployment of RGB and thermal cameras. This dataset includes video footage of five frequently adopted sleep positions: supine, left log, right log, prone left, and prone right, involving the participation of nine individuals. Furthermore, the dataset was collected under two conditions: with and without a blanket. The proposed method begins by normalizing the database to the video frames. Next, the fine-tuned MobileNet-V2 and Inception-V3 models were employed for feature extraction. The tree-seed algorithm was used to select optimal features from the extracted data, reduce dimensionality, and improve the classification performance. Subsequently, Parallel Standard Deviation Padding Max Value (PSPMV), was applied to combine the feature vectors from the RGB and thermal datasets to enhance the accuracy. The fused vectors are then classified using ensemble machine learning models. Our method achieved an accuracy of 97.8% and 98.4% with and without blanket, using subspace KNN. After applying PSPMV fusion to blanket features, the accuracy improved to 99.2%.

The remote sensing (RS) image classification task has been studied widely in the RS and geoscience community. The important applications of RS are landslides, earthquakes, land-use, and land cover classification. Landslides and earthquakes are some of the most dangerous natural disasters that frequently occur. High-resolution RS images can be useful for accurately classifying landslide and earthquake regions. The deep learning technique has improved performance compared with the traditional methods; however, these techniques are reliable on large-scale datasets. In this work, we proposed a novel architecture based on super-resolution and fused bottleneck self-attention called (FMANet) convolutional neural network. A new custom deep super-resolution network is designed as the first step to improve the quality of RS images. In the next step, a new fused bottleneck self-attention architecture is proposed that learns the features in two distinct networks: residual and inverted. Both models are trained on the resultant super-resolution images, whereas the hyperparameters are initialized using Bayesian optimization. In the testing phase, features are extracted from the self-attention layer and passed to the shallow narrow neural network for classification. The experimental process of the proposed architecture is conducted on three datasets, MLRSNet, Bijie Landslide, and Turkey Earthquake, and improved the accuracy of 91.0%, 92.8%, and 99.4%, respectively. Results are also compared with state-of-the-art techniques and show significant improvement and the model is also evaluated using the lime for the interpretation of the outcomes proposed model.

Land-use and land-cover (LULC) classification is an active research challenge in the area of remotely sensed satellite images due to critical applications, such as resource management and agriculture. Deep learning has recently shown a significant improvement in LULC classification using satellite images; however, complex and similar patterns of the images make the classification process more challenging. This article proposes a new information-fused framework for LULC classification from the remotely sensed imaging data. The proposed framework consists of two phases: training and testing. An augmentation process was conducted in the training phase to resolve the imbalance issue. In the next step, two novel convolutional neural network architectures are proposed based on six residual blocks named ResSAN6 and six inverted blocks named RS-IRSAN. The designed models are trained from scratch, whereas the hyperparameters are initialized using the Bayesian optimization algorithm. In the next phase, testing has been performed on the trained models. Testing set images were employed, and deep features from the self-attention layer were extracted. A novel mutual information-based serial fusion approach is proposed that combines both models' features. Also, the variation in the features is removed using median normalization. Furthermore, the feature fusion's computational time and precision rates are improved, which is further optimized using an arithmetic optimization (AO) algorithm. The best information features are selected and finally classified using a shallow wide neural network by employing AOrk. The experimental process of the proposed framework has been performed on three datasets, such as RSI-CB128, WHU-RS19, and NWPU_RESISC45, and achieved an accuracy of 95.7, 97.5, and 92.0%, respectively. Comparing the results with recent related works, the proposed framework shows improved accuracy and precision rates.

Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of feature learning in remote sensing for land use and cover classification, as demonstrated by recent deep learning techniques driven by vast amounts of data. In this work, we proposed a new network-level fusion deep architecture based on 16-tiny Vision Transformer and SIBNet. In the initial phase, data augmentation has been performed to resolve the problem of data imbalances. In the next step, we proposed a self-attention bottleneck-based inception CNN network named SIBNet. In this network, two architectures are followed. The blocks are designed using inception architecture, and each inception module is created with bottleneck blocks. The 16-tiny vision transformer architecture has been implemented for RS images and fused using a network-level fusion with SIBNet for the first time. Hyperparameters of the proposed model have been initialized using Bayesian Optimization for better training on the RS images. After the fusion, the model was on RS image datasets and extracted deep features from the self-attention layer. The extracted features are classified using a neural network classifier with multiple hidden layers. The experimental process of the proposed architecture has been performed on two publically available datasets, such as EuroSAT and NWPU, and obtained an accuracy of 97.8 and 98.9%, respectively. A detailed ablation study has been performed to test the proposed models and shows that the fusion model achieved improved accuracy. In addition, a comparison is conducted with recent techniques and proposed methods, showing improved precision, recall, and accuracy.

Background: As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. Methods: This paper presents a method that combines XAI techniques with a rigorous data-preprocessing pipeline to improve the accuracy and interpretability of ML-based diagnostic tools. Our preprocessing pipeline included outlier removal, missing data handling, and selecting pertinent features based on clinical expert advice. Using R and the caret package (version 6.0.94), we developed and compared several ML algorithms, validated using 10-fold cross-validation and optimized by grid search hyperparameter tuning. XAI techniques were employed to improve model transparency, offering insights into how features contribute to predictions, thereby enhancing clinician trust. Results: Rigorous data-preprocessing improved the models’ generalizability and real-world applicability across diverse clinical datasets, ensuring a robust performance. Neural networks and extreme gradient boosting models achieved the best performance in terms of accuracy, precision, and recall. XAI techniques demonstrated that behavioral features significantly influenced model predictions, leading to greater interpretability. Conclusions: This study successfully developed highly precise and interpretable ML models for ASD diagnosis, connecting advanced ML methods with practical clinical application and supporting the adoption of AI-driven diagnostic tools by healthcare professionals. This study’s findings contribute to personalized intervention strategies and early diagnostic practices, ultimately improving outcomes and quality of life for individuals with ASD.

Falls represent a significant risk factor, necessitating accurate classification methods. This study aims to identify the optimal placement of wearable sensors—specifically accelerometers, gyroscopes, and magnetometers—for effective fall-direction classification. Although previous research identified optimal sensor locations for distinguishing falls from non-falls, limited attention has been given to the classification of fall direction across different body regions. This study assesses inertial measurement unit (IMU) sensors placed at 12 distinct body locations to determine the most effective positions for capturing fall-related data. The research was conducted in three phases: first, comparing classifiers across all sensor locations to identify the most effective; second, evaluating performance differences between sensors placed on the left and right sides of the body; and third, exploring the efficacy of combining sensors from the upper and lower body regions. Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. At the same time, the comparison between the left and right sensor locations shows no significant performance differences within the same anatomical areas. Regarding optimal sensor placement, the findings indicate that sensors positioned on the pelvis and upper legs in the lower body, as well as on the shoulder and head in the upper body, were the most effective results for accurate fall-direction classification. The study concludes that the optimal sensor configuration for fall-direction classification involves strategically combining sensors placed on the pelvis, upper legs, and lower legs.

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications.

Abstract Feature selection and hyper-parameters optimization (tuning) are two of the most important and challenging tasks in machine learning. To achieve satisfying performance, every machine learning model has to be adjusted for a specific problem, as the efficient universal approach does not exist. In addition, most of the data sets contain irrelevant and redundant features that can even have a negative influence on the model’s performance. Machine learning can be applied almost everywhere; however, due to the high risks involved with the growing number of malicious, phishing websites on the world wide web, feature selection and tuning are in this research addressed for this particular problem. Notwithstanding that many metaheuristics have been devised for both feature selection and machine learning tuning challenges, there is still much space for improvements. Therefore, the research exhibited in this manuscript tries to improve phishing website detection by tuning extreme learning model that utilizes the most relevant subset of phishing websites data sets features. To accomplish this goal, a novel diversity-oriented social network search algorithm has been developed and incorporated into a two-level cooperative framework. The proposed algorithm has been compared to six other cutting-edge metaheuristics algorithms, that were also implemented in the framework and tested under the same experimental conditions. All metaheuristics have been employed in level 1 of the devised framework to perform the feature selection task. The best-obtained subset of features has then been used as the input to the framework level 2, where all algorithms perform tuning of extreme learning machine. Tuning is referring to the number of neurons in the hidden layers and weights and biases initialization. For evaluation purposes, three phishing websites data sets of different sizes and the number of classes, retrieved from UCI and Kaggle repositories, were employed and all methods are compared in terms of classification error, separately for layers 1 and 2 over several independent runs, and detailed metrics of the final outcomes (output of layer 2), including precision, recall, f1 score, receiver operating characteristics and precision–recall area under the curves. Furthermore, an additional experiment is also conducted, where only layer 2 of the proposed framework is used, to establish metaheuristics performance for extreme machine learning tuning with all features, which represents a large-scale NP-hard global optimization challenge. Finally, according to the results of statistical tests, final research findings suggest that the proposed diversity-oriented social network search metaheuristics on average obtains better achievements than competitors for both challenges and all data sets. Finally, the SHapley Additive exPlanations analysis of the best-performing model was applied to determine the most influential features.

Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are particularly effective for this task due to their ability to process complex datasets with high accuracy. Furthermore, incorporating explainable artificial intelligence into these predictions provides clarity and interpretability, allowing energy managers and homeowners to make informed decisions that optimize usage and reduce costs. This study comparatively analyzes decision tree-ensemble learning techniques augmented with explainable artificial intelligence for transparency and interpretability in residential building energy consumption forecasting. This approach employs the University Residential Complex and Appliances Energy Prediction datasets, data preprocessing, and decision-tree bagging and boosting methods. The superior model is evaluated using the Shapley additive explanations method within the explainable artificial intelligence framework, explaining the influence of input variables and decision-making processes. The analysis reveals the significant influence of the temperature-humidity index and wind chill temperature on short-term load forecasting, transcending traditional parameters, such as temperature, humidity, and wind speed. The complete study and source code have been made available on our GitHub repository at https://github.com/sodayeong for the purpose of enhancing precision and interpretability in energy system management, thereby promoting transparency and enabling replication.

In this paper, a new analytical approach based on an explicit model with one shape parameter has been proposed to forecast the peak power of the photovoltaic modules (PV) under varying operating conditions. A new analytical model of shape parameter has been developed according the relationship between the parameters of the explicit model and those of the single diode model (SDM). Furthermore, a novel open circuit voltage model as a function of solar irradiation and PV module temperature has been proposed. Temporal variations of PV module temperature and solar irradiation during one reference day have been used to extract the explicit model parameters and then to forecast the peak power for all other days. The proposed method is validated using measured data of different PV module technologies operating outdoor recorded by National Renewable Energy Laboratory (NREL). The results have shown a good agreement between the experimental and the optimized data of the I-V characteristics and the maximum power point for a reference day and the normalized error has not exceed 2.9%. Moreover, the predicted values of maximum power point in two other arbitrary days have a good agreement with experimental ones, and the normalized error has not exceeded 3.6%.

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers’ time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model’s ability to intelligently monitor the irrigation system, which contributes to higher tomato yields.

Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world’s inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R2 for Aus; 0.92 MSE, 90% R2 for Aman; 0.246 MSE, 99% R2 for Boro; 0.062 MSE, 99% R2 for wheat; and 0.016 MSE, 99% R2 for potato production prediction. The Diebold–Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

Abstract Photoplethysmography imaging (PPGI) sensors have attracted a significant amount of attention as they enable the remote monitoring of heart rates (HRs) and thus do not require any additional devices to be worn on fingers or wrists. In this study, we mounted PPGI sensors on a robot for active and autonomous HR (R-AAH) estimation. We proposed an algorithm that provides accurate HR estimation, which can be performed in real time using vision and robot manipulation algorithms. By simplifying the extraction of facial skin images using saturation (S) values in the HSV color space, and selecting pixels based on the most frequent S value within the face image, we achieved a reliable HR assessment. The results of the proposed algorithm using the R-AAH method were evaluated by rigorous comparison with the results of existing algorithms on the UBFC-RPPG dataset (n = 42). The proposed algorithm yielded an average absolute error (AAE) of 0.71 beats per minute (bpm). The developed algorithm is simple, with a processing time of less than 1 s (275 ms for an 8-s window). The algorithm was further validated on our own dataset (BAMI-RPPG dataset [n = 14]) with an AAE of 0.82 bpm.

The optical communication system is preferred over microwave and radio frequency communication systems because of license free operation. Simulative analysis of 10gbps bandwidth using different optical communication channels have been performed in this paper. The different modulation formats of QAM and PSK have been compared for its performances under all the three optical channels OWC, FSO, and LOS-FSO which are an unguided form of optical communication. The optical channels under these modulation formats are extensively used in Digital Video Broadcasting Communication. The parameters such as Q-factor, BER and Eye height can be obtained by varying the wavelengths in the range of 850 nm 1064 nm, 1330 nm and 1550 nm. From the design and performance analysis, the system with the maximum Q-factor and minimum BER can be found for the wavelength of 1064 nm.

[This corrects the article DOI: 10.1371/journal.pone.0250959.].

This paper proposes a hybrid algorithm for capacity configuration optimization of a solar PV-battery-based micro-grid. The hybrid algorithm (BAPSO), which is a combination of Particle Swarm Optimization (PSO) and Bat Algorithm (BA), is designed to optimize the solar generation location and capacity for the efficient performance of a micro-grid. The algorithm considers dynamic transmission power loss optimization and integrates PSO and BA algorithms’ respective advantages to form a hybrid algorithm. The proposed algorithm combines PSO’s fast convergence ability with the less computation time ability of BA to better optimal solution by incorporating the BA’s frequency into the PSO velocity equation to control the pace. The design from the proposed algorithm is tested and validated on IEEE 30 bus test system. The transmission power loss before implementing the algorithm was 22 kW and reduced to 20 kW after the algorithm is used. A further reduction of 0.3 kW of losses is observed by placing the solar generation system at bus number 29.

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper.

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT’s open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. Purity and NMI metrics are used to analyze the performance of the rule based clustering and the accuracy is used for prediction performance. The experimental results show that the proposed work efficiently predicts the disease. The proposed work reaches more than 81% of prediction accuracy compared to the other neural network algorithms.

In healthcare industry, Neural Network has attained a milestone in solving many real-life classification problems varies from very simple to complex and from linear to non-linear. To improve the training process by reducing the training time, Adaptive Skipping Training algorithm named as Half of Threshold (HOT) has been proposed. To perform the fast classification and also to improve the computational efficiency such as accuracy, error rate, etc., the highlighted characteristics of proposed HOT algorithm has been integrated with Strassen’s matrix multiplication algorithm and derived a novel, hybrid and computationally efficient algorithm for training and validating the neural network named as Strassen’s Half of Threshold (SHoT) Training Algorithm. The experimental outcome based on the simulation demonstrated that the proposed SHOT algorithm outperforms both BPN and HOT algorithm in terms of training time which is reduced with the range of 7% to 54% and its efficiency which is improved with the range of 3% to 15% on various dataset such as Hepatitis, SPeCT, Heart, Liver Disorders, Breast Cancer Wisconsin (Diagnostic), Drug Consumption, Cardiotocography, Splice-junction Gene Sequences and Thyroid Disease dataset that are extracted from Machine Learning Dataset Repository of UCI. It can be integrated with any type of supervised training algorithm.

The resource-constrained nature and large-scale adoption of Internet of Things (IoT) have a significant challenge for securing IoT applications. This necessitates a robust and lightweight security architecture and schemes as the existing traditional Internet security architecture and protocols require huge resources and lack of end-to-end security mechanism. In this research, a resource efficient end-to-end security scheme has been proposed by offloading computations and storage of security parameters to fog nodes in the vicinity. In addition, a symmetric-key payload encryption has been used to minimize the overhead of message communication in the resource-contested IoT environment. The analysis shows that the proposed scheme outperforms Transport Layer Security (TLS) in resource usage while it maintains equivalent authenticated end-to-end communication between communicating IoT nodes. The proposed end-to-end security scheme saves more communication bandwidth and incurs less overhead as compared to existing TLS-based security schemes. In particular, the proposed system uses less number of handshakes and achieves a decrease in the number of transmitted messages (approximately 184 bytes as compared to compared TSL message size of 332 bytes) for every handshake. Further, it has been demonstrated through experiments that the proposed security method incurs less overheads as compared to the TLS bandwidth consumption considering a single connection session during message subscription.

In this article, a simple controller design with the fractional order calculations applies to unstable cascade processes with a significant time delay. Series cascade control system consists of two loops in which the inner loop is designed using the IMC principle based on the synthesis method. The primary controller is designed with the FOPI-FOPD controller to ensure stable and satisfactory closed-loop performances for the unstable processes. For the primary controller, five tuning parameters are involved, which tuned using the centroid of the convex stability region method. Different examples are given to illustrate the superiority of the proposed design over some existing designs. Results obtained from simulation reveals that with the proposed control design, enhanced closed-loop control performances are obtained for nominal and perturbed conditions.

Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today's time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed.

The control chart based on mean deviation (MD ) is customary used as a robust alternative to the existing Shewhart control charts for observing changes in dispersion parameter of the process. The existing model of MD control chart is rooted under the assumption that indeterminate observations are not included in measured quality characteristic. If, inspected quality data entail some indeterminate and vague information, typical design of the MD control chart could not be effectively employed. This study originally presents an appropriate generalized design namely neutrosophic mean deviation (gMD) control chart that could accommodate imprecise observations in collected quality characteristic variables. Under the neutrosophic situation, the related properties of this newly gMD design have been derived. Using simulated data, performance of the gMD control chart in terms of neutrosophic average run length ( gARL) is investigated. The performance of proposed gMD control chart relative to existing competitor designs has been evaluated. The study reveals that proposed design of gMD chart outperforms as to existing counterparts in terms of statistical power. To illustrate the efficacy of this new design, real data from a manufacturing company has been used to describe the control procedure of the proposed gMD control chart.

Malware is `malicious software' programs that carry out many of the cyberattacks on the Internet, including cybercrime, fraud, scams and nation-state cyberwar. These malicious software programs come in a wide range of different classifications such as viruses, Trojans, worms, spyware, botnet malware, ransomware, Rootkit, etc. Ransomware is class of malware that holds the victim's data hostage by encrypting the data on a user's computer to make it unavailable to the user and only decrypt it after the user pays a ransom in the form of a sum of money. To avoid detection, different variants of ransomware utilise one or more techniques in their attack flow including Machine Learning (ML) algorithms. There is, therefore, a need to understand the techniques used ransomware development and their deployment strategy in order to understand their attack flow better to develop appropriate countermeasures. In this paper, we propose DNAact-Ran, A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning. DNAact-Ran utilises Digital DNA sequencing design constraints and k-mer frequency vector. To measure the efficacy of the proposed approach, we evaluated DNAact-Run on 582 ransomware and 942 goodware instances to measure the performance of precision, recall, f-measure and accuracy. Compared to other methods, the evaluation results show that DNAact-Run can predict and detect ransomware accurately and effectively.

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance.

This paper proposes a model-free control framework for the path planning of the rigid and soft robotic manipulator using an intelligent algorithm called Weighted Jacobian Rapidly-exploring Random Tree (WJRRT). The optimization approach is used to model the path planning problem, which is independent of the robotic model, and then used the WJRRT algorithm to solve it. WJRRT algorithm not only explores the cartesian space for the end-effector of the robotic manipulator randomly but also directs it towards the goal-position when required. It is robust enough to tackle the uncertainties in the manipulator and make the computation of path planning more efficient. WJRRT assigned a fitness value to each node of the tree. Based on the fitness values algorithm computes the final path, which is a trade-off between efficiency and safety of the path. The simulation results of two, three, and seven degrees of freedom (DOF) robotic manipulators are presented and compared with JT-RRT, Bi-RRT, and TB-RRT algorithms. Experimental results are verified using a soft manipulator made from flexible materials, i.e., polypropylene and polychloroprene. Their flexible structure makes their control complex and creates uncertainties in the model. The simulation and experimental results demonstrate that WJRRT can efficiently and accurately control the motion of manipulators.

Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems. The classification of unseen malware variants with similar characteristics into their respective families is a significant challenge, even if the classifier is trained with known variants belonging to the same family. The identification and extraction of distinct features for each malware is another issue for generalizing the malware detection system. Features that contribute to the generalization capability of the classifier are difficult to be engineered with modifications in each malware. Conventional malware detection systems employ static signature-based methods and dynamic behavior-based methods, which are inefficient in analyzing and detecting advanced and zero-day malware. To address these issues, this work employs a visualization approach where malware is represented as 2D images and proposes a robust machine learning-based anti-malware solution. The proposed system is based on a layered ensemble approach that mimics the key characteristics of deep learning techniques but performs better than the latter. The proposed system does not require hyperparameter tuning or backpropagation and works with reduced model complexity. The proposed model outperformed other state-of-the-art techniques with a detection rate of 98.65%, 97.2%, and 97.43% for Malimg, BIG 2015, and MaleVis malware datasets, respectively. The results demonstrate that the proposed solution is effective in identifying new and advanced malware due to its diverse features.

The continuous improvements in the area of medical imaging, makes the patient monitoring a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect data from human body through sensors, wireless connectivity etc. The junction of medicine and IT like medical informatics will transform healthcare, curbing cost, make more efficient, and saving lives. Various computerized techniques are implemented in the area of Artificial Intelligence (AI) for the application of medical imaging to diagnose the infected regions in the images and videos such as WCE and pathology. The famous stomach infections are ulcer, polyp, and bleeding. Stomach cancer is the most common infection and a leading cause of human deaths worldwide. In the USA, since 2019, a total of 27,510 new cases are reported including 17,230 men and 10,230 women. While the number of deaths is 11,140 consists of 6,800 men and 4,340 women. The manual diagnosis of these stomach infections is a difficult and agitated process therefore it is required to design a fully automated system using AI. In this article, we presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection. In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection. Later, pre-trained deep learning model named VGG16 is employing and re-trained using transfer learning. Features of re-trained model are extracted from two consecutive fully connected layers and fused by array-based approach. Besides, the best individuals are selected through the metaheuristic approach name PSO along mean value-based fitness function. The selected individuals are finally recognized through Cubic SVM. The experiments are conducted on Private collected dataset and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.

A novel mathematical model is envisaged to scrutinize the Darcy Forchheimer 3D Powell Eyring nanofluid flow in a porous medium. Flow is taken under the influence of zero mass flux and convective boundary conditions at the surface and a chemical reaction in the mass equation. The heat transfer flow is scrutinized with non-linear thermal radiation. Entropy generation analysis of the envisioned model is also conducted. The Homotopy Analysis method to yield the series solutions for the envisioned model. The graphs are plotted to witness the characteristics of several parameters versus velocity, heat, and mass distributions and are well cogitated accordingly. The findings show that the velocity is decreasing the function of Darcy-Forchheimer number. Further, the Biot number large values boost the fluid temperature. The outcomes obtained in the analysis are substantiated when compared with a published result in the literature. An outstanding matching is achieved in this regard.

Abstract Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimization-based method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network grid-size, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered.

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7-1.9db) while (35.83-47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

Abstract Disorders of the vestibular system possibly decrease vision, causing abnormal nystagmus and dizziness. To diagnose abnormal nystagmus, various studies have been reported including the use of rotating chair tests and videonystagmography. However, these tests are unsuitable for home use due to their high costs. Thus, a low-cost video-oculography system is necessary to obtain clinical features at home. In this paper, we present a goggle-type low-cost video-oculography system using an infrared camera and Raspberry Pi board for tracking the pupils and evaluating a vestibular system. Horizontal eye movement is derived from video data obtained from an infrared camera and infrared light-emitting diodes, and the velocity of head rotation is obtained from a gyroscope sensor. Each pupil was extracted using a morphology operation and a contour detection method. Rotatory chair tests were conducted with our developed device. To evaluate our system, gain, symmetry, and phase were measured and compared System 2000.

Reversible watermarking has gained importance due to increased involvement of digital media in sensitive fields, such as medical and law enforcement. We propose a prediction error expansionbased watermarking scheme that allows embedding reversible watermark in the image with low distortion. Research work proposes four-phase representation of image which allows exploitation of larger prediction context. We have also proposed a hybrid predictor that helps enhance the prediction accuracy. To reduce image distortion at lower capacity payloads, we use sorting of estimated prediction errors through sorting of prediction context variances. For improvement at higher capacity payloads, adaptive embedding is used to determine whether to embed single or two bits in a given prediction error. The results are compared against some state-of-the-art techniques in the field and show promising results.

The growing concerns in cybersecurity is preventing unknowns which evolve from time to time. Internet of Things (IoT) is one of the emerging fields that have been applied for smart cities and industries. The promises of IoTs could be confronted with the growth in the number and sophistication of cyberattacks. The extension of digital world into physical environment adds new attack surfaces on the existing security threats of traditional Internet. The major challenge brought about by physical connectivity of IoTs is to implement distributed security mechanisms for resource constrain of IoT devices. As an emerging architecture supporting IoT applications, fog computing can be considered to solve the resource and distribution issues in securing fog-to-things communication. Security functions and services, such as cryptography, could be offloaded to fog nodes to reduce computational and storage burdens on IoT devices. The distribution of fog nodes can also solve the scalability of cloud by reducing central processing and communications. On the other hand, lightweight cryptographic functions, such as elliptic curve cryptography, have been proved to be suitable for embedded systems. In this paper, we have analyzed security challenges in terms of cybersecurity principles and proposed a novel encryption scheme for fog-to-things communication.

Abstract We propose novel vehicle detection and classification methods based on images from visible light and thermal cameras. These methods can be used in real-time smart surveillance systems. To classify vehicles by type, we extract the headlight and grill areas from the visible light and thermal images. We then extract texture characteristics from the images and use these as features for classifying different types of moving vehicles. We also extract several features from images obtained at night and during the day, which are the contrast, homogeneity, entropy, and energy. We validated our method experimentally and achieved that the accuracy of our visible image classifier was 92.7% and the accuracy of our thermal image classifier was 65.8% when vehicles were classified into six types such as SUV type, sedan type, RV type.

This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which are validated from x-means and Massachusetts Institute of Technology and Beth Israel Hospital database for the features for AF detection. A smartphone camera was used to obtain photoplethysmography signals. Clinicians labeled 29 records by referring to the electrocardiogram signals. These labeled records were used as a ground truth set to evaluate the accuracy of each method. In the experiments, the kNN, NN, and SVM methods achieved 98.65%, 99.32%, and 97.98% accuracies, respectively.

With an aging population that continues to grow, health care technology plays an increasingly active role, especially for chronic disease management. In the health care market, cloud platform technology is becoming popular, as both patients and physicians demand cost efficiency, easy access to information, and security. Especially for asthma and chronic obstructive pulmonary disease (COPD) patients, it is recommended that pulmonary function test (PFT) be performed on a daily basis. However, it is difficult for patients to frequently visit a hospital to perform the PFT. In this study, we present an application and cloud platform for remote PFT monitoring that can be directly measured by smartphone microphone with no external devices. In addition, we adopted the IBM Watson Internet-of-Things (IoT) platform for PFT monitoring, using a smartphone’s built-in microphone with a high-resolution time-frequency representation. We successfully demonstrated real-time PFT monitoring using the cloud platform. The PFT parameters of FEV1/FVC (%) could be remotely monitored when a subject performed the PFT test. As a pilot study, we tested 13 healthy subjects, and found that the absolute error mean was 4.12 and the standard deviation was 3.45 on all 13 subjects. With the developed applications on the cloud platform, patients can freely measure the PFT parameters without restriction on time and space, and a physician can monitor the patients’ status in real time. We hope that the PFT monitoring platform will work as a means for early detection and treatment of patients with pulmonary diseases, especially those having asthma and COPD.

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

This investigation portrays the transient cylindrical wave diffraction by an oscillating strip. Mathematical analysis of the problem is carried out with the help of an integral transforms and the Wiener–Hopf technique. Using far zone approximation, the scattered field is evaluated by the method of steepest descent. This study takes into consideration the transient cylindrical source and an oscillating strip such that both the source and a scatterer have different oscillating frequencies ω 1 ′ and ω 0 ′ , respectively. The situation under consideration is well supported by graphical results showing the effects of emerging parameters.