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Abstract Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.

Abstract Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.

Abstract Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value

Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

The journal retracts the article, “Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology” [...]

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate.

Abstract High concentrations of potentially toxic elements (PTEs) in potable water can cause severe human health disorders. Present study examined the fitness of groundwater for drinking purpose based on the occurrence of nine PTEs in a heavy pilgrim and tourist influx region of the Garhwal Himalaya, India. The concentrations of analyzed PTEs in groundwater were observed in the order of Zn > Mn > As > Al > Cu > Cr > Se > Pb > Cd. Apart from Mn and As, other PTEs were within the corresponding guideline values. Spatial maps were produced to visualize the distribution of the PTEs in the area. Estimated water pollution indices and non-carcinogenic risk indicated that the investigated groundwater is safe for drinking purpose, as the hazard index was

Electronic waste generation is increasing dramatically throughout the world. Consequently, this increase in E-waste harms the environment, health, and other aspects of human life. Moreover, hazardous substances and the informal disposal of E-waste severely threaten human health and the environment. Saudi Arabia is the largest Arab country in terms of electronic waste generation and is the Arab country that generates the most E-waste. Over the past few decades, several initiatives and policy implementations have been undertaken in the country. However, the management of E-waste is still a source of distress and an unresolved issue. Sustainable development requires much more effort, primarily efficient E-waste management, which can only be achieved by establishing a formal collection system, early forecasting, and accurate estimations. The purpose of this study is to provide an overview of the fundamental and emerging trends in E-waste production worldwide and in Saudi Arabia. This paper aims to summarize the hazardous elements present in E-waste, their dangerous effects, and the economic potential for recycling E-waste as a means of developing sustainable communities. This study explores the practices regarding efficient E-waste management and E-waste estimation and prediction globally, specifically in Saudi Arabia and other Arab countries. This study suggests that more than the use of a single management probe will be needed to achieve significant results. Instead, a complementary approach can be used to achieve the most effective results. Additionally, people should be aware of the importance of handling and recycling E-waste. This study emphasizes the importance of formal collection and documentation to ensure effective monitoring and sustainable development in any developed country.

Given that the implementation of renewable technologies has some key bottlenecks in adoption, this topic has been explored. Particularly, we are reviewing existing theories and models to understand their fit for changing social structures and evolving world contexts. This review begins with an introduction followed by a background study on renewable energy technology (RET). We have employed a mixed-approach methodology to synthesize the relevant literature. The review comprises a summary and comparison of some existing theories and models such as TAM, TRA, and UTAUT, elucidating factors influencing technology adoption processes. Additionally, the review discusses the scope for future research, emphasizing the need for more nuanced frameworks that account for contextual intricacies and emerging trends in renewable energy adoption. Ultimately, the review concludes with insights into the ongoing discourse surrounding energy technology acceptance and recommendations on the inclusion of current world views in the scope for future study.

Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%.

Abstract The occurrence of heavy metals (HMs) in drinking water has been a critical water quality concern for a long time and can compromise its aesthetic value to the larger extent. Chronic exposure of human beings to these toxic and non-toxic HMs through water ingestion can result in significant health risks. To assess these associated health risks, the present study was planned, designed and carried out for analyses of nine HMs namely, Al, Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb in the potable water samples collected from different sources located across the Mandakini valley of Garhwal Himalaya, India using Inductively Coupled Plasma Mass Spectrometry. The measured values of Al, Cr, Mn, Co, Ni, Cu, Zn, Cd and Pb were found in the range of BDL–27.4 µg l−1, 0.26–4.5 µg l−1, BDL–139 µg l−1, 0.02–0.9 µg l−1, 0.4–5.5 µg l−1, 0.07–9.2 µg l−1, BDL–4164 µg l−1, BDL–0.8 µg l−1, and BDL–11.2 µg l−1, respectively. The observed values of analyzed HMs except Zn and Pb were found below the reference values prescribed by the WHO, USEPA and BIS. In addition, Zn concentration exceeded its maximum permissible limit (4000 µg l−1) recommended by WHO for infants at one station only. The observed indices show that there are no health risks from HMs contamination via drinking water in the region. Moreover, the estimated hazard quotients for children and adults also revealed no potential health risks. The results of present study will be useful as baseline data for state and national regulatory agencies.

Anemia is a universal public health issue, which occurs as the result of a reduction in red blood cells. This disease is common among children in Africa and other developing countries. If not treated early, children may suffer long-term consequences such as impairment in social, emotional, and cognitive functioning. Early detection of anemia in children is highly desirable for effective treatment measures. While there has been research into the development of computer-aided diagnosis (CAD) systems for anemia diagnosis, a significant proportion of these studies encountered limitations when working with limited datasets.To overcome the existing issues, this paper proposes a large dataset, named CP-AnemiC, comprising 710 individuals (range of age, 6–59 months), gathered from several hospitals in Ghana. The conjunctiva image-based dataset is supported with Hb levels (g/dL) annotations for accurate diagnosis of anemia. A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels (g/dL) based on the conjunctival pallor images. This paper conducts a comprehensive experiment on the CP-AnemiC dataset. The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels (g/dL) estimation.

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

Analysing and understanding donor behaviour in nonprofit organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an artificial-intelligence-enabled decision-support system (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a design science research approach to evaluate an AI-enabled DSS’s initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment by conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The evaluation of the conceptual design results led to the recommendation for efficiency, effectiveness, flexibility, and usability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSSs for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such an analysis of donors who donate money or volunteer time for NPOs.

Supply Chain processes are continuously marred by myriad factors including varying demands, changing routes, major disruptions, and compliance issues. Therefore, supply chains require monitoring and ongoing optimization. Data science uses real-time data to provide analytical insights, leading to automation and improved decision making. RFID is an ideal technology to source big data, particularly in supply chains, because RFID tags are consumed across supply chain process, which includes scanning raw materials, completing products, transporting goods, and storing products, with accuracy and speed. This study carries out a systematic literature review of research articles published during the timeline (2000-2021) that discuss the role of RFID technology in developing decision support systems that optimize supply chains in light of Industry 4.0. Furthermore, the study offers recommendations on operational efficiency of supply chains while reducing the costs of implementing the RFID technology. The core contribution of this paper is its analysis and evaluation of various RFID implementation methods in supply chains with the aim of saving time effectively and achieving cost efficiencies.

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.

Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.

Alzheimer’s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer’s disease (AD), it does not necessarily show the obvious symptoms of AD. As a result, it becomes challenging to distinguish between mild cognitive impairment and cognitively normal. In this paper, we propose an ensemble of deep learners based on convolutional neural networks for the early diagnosis of Alzheimer’s disease. The proposed approach utilises simple averaging ensemble and weighted averaging ensemble methods. The ensemble-based transfer learning model demonstrates enhanced generalization and performance for AD diagnosis compared to traditional transfer learning methods. Extensive experiments on the OASIS-3 dataset validate the effectiveness of the proposed model, showcasing its superiority over state-of-the-art transfer learning approaches in terms of accuracy, robustness, and efficiency.

Abstract This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise. Article Highlights This article deals with MEMS tunable capacitor dynamics and modeling, considering measurement noise. It designs and applies fuzzy PID control system for regulating MEMS voltage reference output. This paper contributes to robustness increase in pull-in performance of the tunable capacitor.

Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods.

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel and global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

Abstract Recently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for the smart product development in the next-generation WSNs environments.

In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem: i) X-ray energies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required; ii) X-ray multi-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; and iii) X-ray computer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.

Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.

Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.

Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.

In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Abstract Background Accumulating evidence supports the view that an imbalance of gut bacteria contributes to IBS, and that increasing the mass of beneficial species may reduce the numbers of pathogenic bacteria and help alleviate symptoms. Methods In this double-blind trial 400 adult patients with moderate-to-severe symptomatic diarrhea-predominant IBS (IBS-D) were randomized to treatment with the multi-strain probiotic Bio-Kult® (14 different bacterial strains) or placebo for 16 weeks. The change in severity and frequency of abdominal pain was the primary outcome measure. Results Probiotic treatment significantly improved the severity of abdominal pain in patients with IBS-D. A 69% reduction for probiotic versus 47% for placebo (p

Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a batterypowered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-term tracing and research of personal sleep monitoring at home.

Task switching is a common method to investigate executive functions such as working memory and attention. This paper investigates the effect of acute stress on brain activity using task switching. Surprisingly few studies have been conducted in this area. There is behavioral and physiological evidence to indicate that acute stress makes the participants more tense which results in a better performance. In this current study, under stressful conditions, the participants gave quick responses with high accuracy. However, unexpected results were found in relation to salivary cortisol. Furthermore, the electroencephalogram results showed that acute stress was pronounced at the frontal and parietal midline cortex, especially on the theta, alpha, and gamma bands. One possible explanation for these results may be that the participants changed their strategy in relation to executive functions during stressful conditions by paying more attention which resulted in a higher working memory capacity which enhanced performance during the task switching.

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.

Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this paper, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power, which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.

Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for lowpower sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.

Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.

Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.

Extensive investigation has been performed in location-centric or geocast routing protocols for reliable and efficient dissemination of information in Vehicular Adhoc Networks (VANETs). Various location-centric routing protocols have been suggested in literature for road safety ITS applications considering urban and highway traffic environment. This paper characterizes vehicular environments based on real traffic data and investigates the evolution of location-centric data dissemination. The current study is carried out with three main objectives: (i) to analyze the impact of dynamic traffic environment on the design of data dissemination techniques, (ii) to characterize location-centric data dissemination in terms of functional and qualitative behavior of protocols, properties, and strengths and weaknesses, and (iii) to find some future research directions in information dissemination based on location. Vehicular traffic environments have been classified into three categories based on physical characteristics such as speed, inter-vehicular distance, neighborhood stability, traffic volume, etc. Real traffic data is considered to analyze on-road traffic environments based on the measurement of physical parameters and weather conditions. Design issues are identified in incorporating physical parameters and weather conditions into data dissemination. Functional and qualitative characteristics of location-centric techniques are explored considering urban and highway environments. Comparative analysis of location-centric techniques is carried out for both urban and highway environments individually based on some unique and common characteristics of the environments. Finally, some future research directions are identified in the area based on the detailed investigation of traffic environments and location-centric data dissemination techniques.

A Wireless Sensor Network (WSN) consists of enormous amount of sensor nodes. These sensor nodes sense the changes in physical parameters from the sensing range and forward the information to the sink nodes or the base station. Since sensor nodes are driven with limited power batteries, prolonging the network lifetime is difficult and very expensive, especially for hostile locations. Therefore, routing protocols for WSN must strategically distribute the dissipation of energy, so as to increase the overall lifetime of the system. Current research trends from areas, such as from Internet of Things and fog computing use sensors as the source of data. Therefore, energy-efficient data routing in WSN is still a challenging task for realtime applications. Hierarchical grid-based routing is an energy-efficient method for routing of data packets. This method divides the sensing area into grids and is advantageous in wireless sensor networks to enhance network lifetime. The network is partitioned into virtual equal-sized grids. The proposed mode-switched grid-based routing protocol for WSN selects one node per grid as the grid head. The routing path to the sink is established using grid heads. Grid heads are switched between active and sleep modes alternately. Therefore, not all grid heads take part in the routing process at the same time. This saves energy in grid heads and improves the network lifetime. The proposed method builds a routing path using each active grid head which leads to the sink. For handling the mobile sink movement, the routing path changes only for some grid head nodes which are nearer to the grid, in which the mobile sink is currently positioned. Data packets generated at any source node are routed directly through the data disseminating grid head nodes on the routing path to the sink.

Gastroduodenal tuberculosis is uncommon even in parts of world where tuberculosis is endemic and accounts for 2.3 % of abdominal tuberculosis. We present here a case of duodenal tuberculosis who presented with vomiting, pain abdomen and weight loss. Duodenoscopy revealed mucosal ulceration and nodularity with marked enlarged and erythematous surrounding folds. Histopathology of duodenum showed features of tuberculosis.

Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets.

Indoor radon and thoron concentration plays a vital role in the total effective dose in the indoor environments. In the present study, the measurement of indoor radon, soil gas radon concentration and the drinking water radon concentration was carried out in Rajpur area of Dehradun valley located near by the geological fault line named Main Boundary Thrust (MBT). The measurement was carried out using RAD-7, a solid state detector with its special accessory. The indoor radon concentration varies from 35 to 150 Bqm−3 with an average value of 85 Bqm−3. The soil-gas radon concentration varies from 2 to 12.3 kBqm−3 with an average value of 6.5 kBqm−3. Radon concentration in water samples varies from 1.7 to 57.7 kBqm−3 with an average value of 20 kBqm−3. These results are helpful for estimation of annual effective dose, ingestion dose and inhalation doses. The annual effective dose varies from 0.88 to 3.78 mSvy−1 with an average value of 2.13 mSvy−1. The annual ingestion dose due to drinking water was found to vary from 0.36 to 7.91 mSvy−1 with an average value of 3.92 mSvy−1. The annual inhalation dose was found to vary from 0.0042 to 0.1454 mSvy−1 with an average of 0.0504 mSvy−1.

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.