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The most prevalent type of cancer worldwide is mouth cancer. Around 2.5% of deaths are reported annually due to oral cancer in 2023. Early diagnosis of oral squamous cell carcinoma (OSCC), a prevalent oral cavity cancer, is essential for treating and recovering patients. A few computerized techniques exist but are focused on traditional machine learning methods, such as handcrafted features. In this work, we proposed a fully automated architecture based on Self‐Attention convolutional neural network and Residual Network information fusion and optimization. In the proposed framework, the augmentation process is performed on the training and testing samples, and then two developed deep models are trained. A self‐attention MobileNet‐V2 model is developed and trained using an augmented dataset. In parallel, a Self‐Attention DarkNet‐19 model is trained on the same dataset, whereas the hyperparameters have been initialized using the whale optimization algorithm (WOA). Features are extracted from the deeper layers of both models and fused using a canonical correlation analysis (CCA) approach. The CCA approach is further optimized using an improved WOA version named Quantum WOA that removes the irrelevant features and selects only important ones. The final selected features are classified using neural networks such as wide neural networks. The experimental process is performed on the augmented dataset that includes two sets: 100× and 400×. Using both sets, the proposed method obtained an accuracy of 98.7% and 96.3%. Comparison is conducted with a few state‐of‐the‐art (SOTA) techniques and shows a significant improvement in accuracy and precision rate. [ABSTRACT FROM AUTHOR]

Deep learning has recently become a viable approach for classifying Alzheimer's disease (AD) in medical imaging. However, existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness classification. To address these issues, a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is proposed. An upgraded primary C3D network is utilised to create rougher low‐level feature maps. It introduces a new convolution block that focuses on the structural aspects of the magnetic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map output. Then, several fully connected layers are used to achieve multi‐task learning, generating three outputs, including the primary classification task. The other two outputs employ backpropagation during training to improve the primary classification job. Experimental findings show that the authors' proposed method outperforms current approaches for classifying AD, achieving enhanced classification accuracy and other indicators on the Alzheimer's disease Neuroimaging Initiative dataset. The authors demonstrate promise for future disease classification studies. [ABSTRACT FROM AUTHOR]

To aid in detection of tuberculosis, researchers have concentrated on developing computer‐aided diagnostic technologies based on x‐ray imaging. Since it generates noninvasive standard‐of‐care data, a chest x‐ray image is one of the most often used diagnostic imaging modalities in computer‐aided solutions. Due to their significant interclass similarities and low intra‐class variation abnormalities, chest x‐ray pictures continue to pose difficulty for proper diagnosis. In this paper, a novel automated framework is proposed for the classification of tuberculosis, COVID‐19, and pneumonia from chest x‐ray images using deep learning and improved optimization technique. Two pre‐trained convolutional neural network models such as EfficientB0 and ResNet50 have been utilized and fine‐tuned based on the additional layers. Both models are trained with fixed hyperparameters on the selected datasets and obtained newly trained models. A novel feature selection technique has been proposed that selects the best features. In the novel version, distance and update position formulation has been modified. The selected features are further fused using a novel technique that is based on the serial and standard deviation threshold function. The experimental process of the proposed framework is conducted on three datasets and obtained an accuracy of 98.2%, 99.0%, and 98.7%, respectively. In addition, a detailed Wilcoxon signed‐rank analysis is conducted and shows the proposed method significance performance. Based on the results, it is concluded that the proposed method accuracy is improved after the fusion process. In addition, the comparison with recent techniques shows the proposed method as more significant in terms of accuracy and precision rate. [ABSTRACT FROM AUTHOR]

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%. [ABSTRACT FROM AUTHOR]

Retraction: [Aryan Marwaha, Rami Qays Malik, Shehab Mohamed Beram, Ali Rizwan, Hari Kishore Kakarla, Deepak Thakur, Tanya Gera, Mohammad Shabaz, Visualisation‐based binary classification of android malware using vgg16, IET Software 2023 (https://doi.org/10.1049/sfw2.12094)]. The above article from IET Software, published online on 23 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Retraction: [Muhammad Shafiq, Fatemah H. Alghamedy, Nasir Jamal, Tahir Kamal, Yousef Ibrahim Daradkeh, Mohammad Shabaz, Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality, IET Software 2023 (https://doi.org/10.1049/sfw2.12091)]. The above article from IET Software, published online on 6 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Retraction: [Ankur Gupta, Purnendu Prabhat, Sahil Sawhney, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Mohammad Shabaz, Robotic Process Automation use cases in academia and early implementation experiences, IET Software 2022 (https://doi.org/10.1049/sfw2.12061)]. The above article from IET Software, published online on 19 May 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

The above article from IET Communications, published online on 28 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

The above article from IET Communications, published online on 12 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Medical internet of things (IoT) includes various devices, sensor, machines, equipment which exchange data over wide network. Industry 5.0 and 5G technology have ramped mIoT data and cost‐effective sensors. There is sudden upsurge in the medical IoT for enhancing the medical care. Integration of cloud server for data storage and cloud computing has led to the emergence of time and cost‐effective management of medical resources and improved patient lifestyle. However, cloud data is always at risk of data leak and malicious attack by unwanted users for their personal gains and agenda. With the increase of data exchange in the medical field, there is an urgency of keeping all the transaction safe and secure. There is a growing malicious data attacks and vulnerability. Therefore, to overcome such situations, the proposed model is a step towards sustainable technology. Keeping the focus on cost effective data security, the present work has developed a framework of modified Lamport Merkle Digital Signature method for signature generation and verification. It makes use of central healthcare controller (CHC) which determine the root of generated signature along with verification and authentication. For verification, the validation hash public key with generate key is required to validate the signature. This led to the efficient, cost effective and faster security when compared to the existing methods. [ABSTRACT FROM AUTHOR]

A distributed lossless coding system based on cloud computing is proposed to increase the video transcoding capacity of the new media interactive broadcasting project in the research department. Distributed transcoding is carried out by using the programming ideas of two Hadoop cores: HDFS (Hadoop distributed file system) and map reduce. At the same time, the specific process of distributed transcoding is introduced and designed in detail. The experimental results show that the efficiency of the distributed transcoding scheme is greatly improved. The results show that this experiment selects the 32 m with the best segment size as the benchmark, and calculates that the distributed transcoding time is 60.3% higher than the traditional single machine transcoding. This improvement significantly improves the transcoding efficiency, improves the experience effect of video service, and adapts to the trend of the rapid development of new media in the current era. The design of the system architecture not only greatly improves the transcoding efficiency, but also saves the transcoding time. [ABSTRACT FROM AUTHOR]

With the proliferation of massive varieties and unknown malicious viruses, it is difficult to achieve effective and timely defence by anti‐virus technology with virus signature matching as the core. Hence, the research of malicious code detection and defence is investigated. Firstly, it introduces the defence technology and detection technology. The simulation results show that the default infection probability of the simulation program is 15.1%. The default detection probability is 60.1%, the default number of nodes is 1000, the default time step is 1000 ms, and the default initial infected node is only node 0, that is, only the malicious code on node 0 is activated. After start‐up, the whole P2P starts to run, and the number of infected nodes increases. Finally, all nodes are infected. The infection speed becomes faster and faster. When the number of infected nodes reaches about half, the infection speed begins to slow down. [ABSTRACT FROM AUTHOR]

Railway turnout is the most important line connection equipment of high‐speed railway system. In order to improve the monitoring and maintenance of high‐speed railway turnout gap, we avoid the large impact of train operations on the turnout. With the increase of train speed, the impact of train operation on turnout is increasing. Therefore, the requirements for monitoring, maintenance, and adjustment of high‐speed railway turnout gap are also gradually improved. In this paper, the feature recognition research of mechatronic railway turnout state signal based on CMOS area array technology is proposed. First, it introduces the collection of turnout health parameters, the collection and processing of gap data in turnout based on CMOS, and the image feature recognition algorithm of adaptive threshold. The problem of inconvenient collecting the bifurcation image showing the gap is solved, and the pollution affects the accuracy. The experimental results show that, according to the fault mechanism of turnout system, turnout faults are mainly divided into electrical faults and mechanical faults. On‐site maintenance: the records show that the mechanical failure accounts for more than 90% of turnout failures. Fault category is the database of fault diagnosis model training. According to the structural principle and fault record description of turnout, there are 12 categories of turnout faults. The feature recognition of mechatronic railway turnout state signal based on CMOS area array technology proposed in this paper has certain validity and accuracy. [ABSTRACT FROM AUTHOR]

Multiagent has become a multidomain intersecting hot issue as artificial intelligence technology has advanced in the industrial sector. Multiagent system creation control has lately undergone a lot of academic research, and it has got applications in a wide range of fields, including drag reduction, monitoring, telecommunications relay, and searching. A matrix theory using algebraic graph theory for automatic control principle learning is proposed based on multiagent system collaborative control strategy method to study the application of PID control method in human-machine cluster multi-intelligence system. The method first presents the determination of the stability set of the low-order controller parameters for the multidelay single-input single-output system with complex coefficients; through matrix theory, the multi-intelligence system is decomposed into multiple subsystems and the problem is transformed into subsystem stability analysis problem; thus, the complexity of the system is reduced. It has been proved that the power system device adopted by the UAV hardware platform can make the UAV flight time up to 24.7 min and additional load up to 1.5 kg. Based on the simulation analysis of PID control algorithm, the PID parameters of UAV are adjusted, which improves the parameter tuning efficiency and enhances the response speed of UAV to error and the stability of motion. The experimental findings suggest that the UAV hardware platform in the UAV control system has high dynamic performance. Good PID settings allow the UAV to respond to control orders quickly and correctly; also, the data received by the sensor decrease the complexity of operating several UAVs at the same time and minimize the operators' burden. [ABSTRACT FROM AUTHOR]

Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented. [ABSTRACT FROM AUTHOR]

VANETs (vehicular ad hoc networks) have evolved as a platform for enabling intelligent inter-vehicle communication while also improving traffic safety and performance. VANETs are a difficult research topic because of the road dynamics, high mobility of cars, their unlimited power supply, and the growth of roadside wireless infrastructures. In wireless networks, game theory approaches have been widely used to investigate the interactions between competitive and cooperative behavior. In this research, we propose a technique for vehicular ad hoc networks that uses a game theory approach to automate vehicle grouping and cluster head nomination. This will eliminate the need for cluster reformation on a regular basis. Furthermore, each vehicle's social behavior will be exploited to establish clusters in the vehicular environment. For the development of clusters on the social behavior of the cars, a machine learning approach (K-means algorithm) is applied. The proposed system is tested against a variety of characteristics, including CH life time, average cluster member life time, average number of reaffiliation times, throughput, and packet loss rate, and the results indicate that the VANET performed very well with high accuracy in validation and testing, and overall in the range of 0.97 to 0.99. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial (p = 1.43) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively (p < 0.0001) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life. [ABSTRACT FROM AUTHOR]

Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual's health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient's stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient's kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems. [ABSTRACT FROM AUTHOR]

Cloud computing technology has brought tremendous evaluation in the arena of IT (information technology). This technology paves the path of starting business with lowest investment by availing infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS) pay per uses model. Cloud computing services can be quickly and easily provisioned and discharged with minimum efforts and service provider (SP) relationship. Cloud computing characteristics such as on demand self-service, broad network access, resource pooling, and rapid elasticity lead the demand of computing. Despite these features, this platform is free to security issues and attacks specifically in terms of communication because of unsecure authentication and privacy. However, strong user authentication procedure impedes illegal access to the SP which is the principal requirement for securing cloud computing ecosystem. In this regard, we attempt to propose possible counter measures for the cloud ecosystem. Hence, this paper presented a novel one way hash and nonce-based two-factor secure authentication scheme with traditional user IDs, password, and OTP verification procedure that resist brute force attack, session and account hijacking attack, MITM attacks, and replay attacks. [ABSTRACT FROM AUTHOR]

The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies. [ABSTRACT FROM AUTHOR]

Agriculture is critical to human life. Agriculture provides a means of subsistence for a sizable portion of the world's population. Additionally, it provides a large number of work opportunities for inhabitants. Many farmers prefer traditional farming approaches, which result in low yields. Agriculture and related industries are vital to the economy's long-term growth and development. The primary issues in agricultural production include decision-making, crop selection, and supporting systems for crop yield enhancement. Agriculture forecasting is influenced by natural variables such as temperature, soil fertility, water volume, water quality, season, and crop prices. Growing advancements in agricultural automation have resulted in a flood of tools and apps for rapid knowledge acquisition. Mobile devices are rapidly being used by everyone, including farmers. This paper presents a framework for smart crop tracking and monitoring. Sensors, Internet of Things cameras, mobile applications, and big data analytics are all covered. The hardware consists of an Arduino Uno, a variety of sensors, and a Wi-Fi module. This strategy would result in the most effective use of energy and the smallest amount of agricultural waste possible. [ABSTRACT FROM AUTHOR]

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies. [ABSTRACT FROM AUTHOR]

Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient's behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO2 sensor. [ABSTRACT FROM AUTHOR]

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics. [ABSTRACT FROM AUTHOR]

This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells. [ABSTRACT FROM AUTHOR]

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance. [ABSTRACT FROM AUTHOR]

Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient's behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO2 sensor. [ABSTRACT FROM AUTHOR]

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY. [ABSTRACT FROM AUTHOR]

The major health hazards from smoke and dust are due to microscopic fine particles present in smoke as well as in dust. These fine particles, which are microscopic in nature, can penetrate into human lungs and give rise to a range of health problems such as irritation in eyes, a runny nose, throat infection, and chronic cardiac and lung diseases. There is a need to device such mechanisms that can monitor smoke in thermal power plants for timely control of smoke that can pollute air and affects adversely the people living nearby the plants. In order to solve the problems of low accuracy of monitoring results and long monitoring time in conventional methods, a real-time smoke and dust monitoring system in thermal power plants is proposed, which makes use of modified genetic algorithm (GA). The collection and calibration of various monitoring parameters are accomplished through sampling control. The smoke and dust emission real-time monitoring subsystems are employed for the monitoring in an accurate manner. A dual-channel TCP/IP protocol is used between remote and local controlling modules for secure and speedy communication of the system. The generic GA is improved on the basis of the problem statement, and the linear programming model is used to avoid the defect of code duplication with genetic operations. The experimental results show that the proposed smoke and dust monitoring system can effectively improve the accuracy of the monitoring results and also reduce the time complexity by providing solutions in a faster manner. The significance of the proposed technique is to provide a reliable basis for the smoke and dust emission control of thermal power plants for safeguarding the human health. [ABSTRACT FROM AUTHOR]

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness. [ABSTRACT FROM AUTHOR]

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches. [ABSTRACT FROM AUTHOR]

Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages. [ABSTRACT FROM AUTHOR]

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots' temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window's mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented. [ABSTRACT FROM AUTHOR]

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields. [ABSTRACT FROM AUTHOR]

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions. [ABSTRACT FROM AUTHOR]

In order to study the mechanical properties and durability of cement-based composite with carbon nanotube, the test and analysis experiments are designed. Raw materials and related pharmaceutical instruments are prepared, to obtain cement-based composite with carbon nanotube samples by catalytic pyrolysis according to different proportions. The prepared sample is taken as the experimental object, and different bearing capacities are applied on different positions of the sample, to observe the change of the sample, and then, the experimental results of the mechanical properties of composite materials are obtained. The durability test results are obtained by combining the impermeability and frost resistance of the test object. The average compressive strength is 84.09 MPa, the average flexural strength is 16.9 MPa, and the crack resistance index is 22.5. In addition, the structure and diffusion coefficient of the sample also change in different degrees after the solution immersion and freeze-thaw treatment. Through longitudinal comparison, the more the carbon nanotubes are added into cement-based composite, the better its mechanical properties and durability are. [ABSTRACT FROM AUTHOR]

The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device. Device identification and classification in M2M for secure telerobotic surgery are presented in this study. Telerobotics is an important aspect of the telemedicine industry. The major purpose is to provide remote medical care, which eliminates the requirement for both doctors and patients to be in the same location. This paper aims to propose a security and energy-efficient protocol for telerobotic surgeries, which is the primary concern at present. For secure telerobotic surgery, the author presents an Efficient Device type Detection and Classification (EDDC) protocol for device identification and classification in M2M communication. The periodic trust score is calculated using three factors from each sensor node. It demonstrates that the EDDC protocol is more effective and secure in detecting and categorizing rogue devices. [ABSTRACT FROM AUTHOR]

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained. [ABSTRACT FROM AUTHOR]

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique. [ABSTRACT FROM AUTHOR]

Background. In this day and age, 17% of children less than 5 years of age died of pneumonia; it is the common cause of children death. It is one of the main children respiratory infectious diseases, i.e., mycoplasma pneumonia (MP). The imaging examination can be adopted to quickly observe the morphology and scope of the pulmonary lesions and know the effect of disease treatment and subsequent changes in the disease in order to provide a basis for treatment. Therefore, the most commonly applied technology for detecting pneumonia in children is imaging technology, including chest X-ray and CT. Objectives. The main objective of the work is to investigate the chest computed tomography (CT) findings of children patients with Mycoplasma pneumoniae pneumonia (MPP) and MP combined with streptococcal pneumonia (SP). The mixed infection of MP and SP is very common clinically, and the diagnosis of this type of mixed pneumonia is a critical research topic faced by pediatric respiratory physicians. The comparison is done on the incidence of bronchial and pulmonary interstitial lesions, the degree of lymph node enlargement, the volume and depth of pleural effusion, and the location and morphology of the pulmonary lesions in the chest CT images of children patients from the two groups. Methods. There were comparisons on the incidence of bronchial and pulmonary interstitial lesions, the degree of lymph node enlargement, the volume and depth of pleural effusion, and the location and morphology of the pulmonary lesions in the chest CT images of children patients from the two groups. All the experiments are done in the MATLAB. Results. The results showed that the proportions of reticular shadow, ground glass shadow, bronchial inflation phase, tube wall thickening, and vascular bundle thickening on the CT images of children patients from the MPP group were dramatically higher than those of the MP + SP group (P < 0.05). The maximum transverse diameter of enlarged lymph node in children patients from the MPP group was obviously larger than the diameter of the MP + SP group (P < 0.05). The number of children patients with pleural effusion was 22 in the MP + SP group, which was greatly higher than the MPP group (P < 0.05). Conclusion. In conclusion, the chest CT images of children patients from the MPP group were mainly pulmonary interstitial changes. Furthermore, the alveolar inflammation could be observed on the CT images shown when children patients were combined with SP infection. The more obvious manifestations were that the flaky shadows appeared in the lungs, the pleural effusion became thicker, and the transverse diameters of enlarged lymph nodes were bigger. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the recent innovations in Information Technology, which intends to interconnect the physical and digital worlds. It introduces a vision of smartness by enabling communication between objects and humans through the Internet. IoT has diverse applications in almost all sectors like Smart Health, Smart Transportation, and Smart Cities, etc. In healthcare applications, IoT eases communication between doctors and patients as the latter can be diagnosed remotely in emergency scenarios through body sensor networks and wearable sensors. However, using IoT in healthcare systems can lead to violation of the privacy of patients. Thus, security should be taken into consideration. Blockchain is one of the trending research topics nowadays and can be applied to the majority of IoT scenarios. Few major reasons for using the Blockchain in healthcare systems are its prominent features, i.e., Decentralization, Immutability, Security and Privacy, and Transparency. This paper's main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain. So, initially, a brief introduction to the basic concepts of IoT and Blockchain is provided. After this, the applicability of IoT and Blockchain in the medical sector is explored in three major areas—drug traceability, remote patient-monitoring, and medical record management. At last, the challenges of deploying IoT and Blockchain in healthcare systems are discussed. [ABSTRACT FROM AUTHOR]

Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results. [ABSTRACT FROM AUTHOR]

The advancement of Internet technologies has led to the availability of audios, images, and videos in different forms. The unauthorized users are exploiting the use of multimedia by transmitting them on various Internet sites to earn money unethically without the intervention of the original copyright holder. Watermarking is a technique used to hide the signal known as watermark inside multimedia data that is not visible to the intruder to manipulate any information. In this paper, a secured watermarking approach is developed to tackle issues related to copyright protection and ownership identification. A Secured Graph Based Transform, Singular Valued Decomposition, and Hyperchaotic Encryption hybrid techniques are proposed. The watermark cannot be embedded in every frame of the video as it adds to the size of the video and watermark can be easily retrieved by an intruder. Therefore, the frame selection algorithm has been proposed in the given work. Adding watermark in the frame adds to the challenge of quality loss. The quality loss is addressed in this work. Various attacks have been applied on the watermarked frames to calculate the performance of the proposed technique using quality metrics: Peak Signal to Noise Ratio, Structural Similarity Index, Normalized Correlation, and Bit Error Rate. The results indicate that the proposed technique is effective against various attack scenarios. [ABSTRACT FROM AUTHOR]

Sorting is one of the operations on data structures used in a special situation. Sorting is defined as an arrangement of data or records in a particular logical order. A number of algorithms are developed for sorting the data. The reason behind developing these algorithms is to optimize the efficiency and complexity. The work on creating new sorting approaches is still going on. With the rise in the generation of big data, the concept of big number comes into existence. To sort thousands of records either sorted or unsorted, traditional sorting approaches can be used. In those cases, we can ignore the complexities as very minute difference exists in their execution time. But in case the data are very large, where execution time or processed time of billion or trillion of records is very large, we cannot ignore the complexity at this situation; therefore, an optimized sorting approach is required. Thus, SA sorting is one of the approaches developed to check sorted big numbers as it works better on sorted numbers than quick sort and many others. It can also be used to sort unsorted records as well. [ABSTRACT FROM AUTHOR]

Detection of abandoned and stationary objects like luggage, boxes, machinery, and so forth, in public places is one of the challenging and critical tasks in the video surveillance system. These objects may contain weapons, bombs, or other explosive materials that threaten the public. Though various applications have been developed to detect stationary objects, different challenges, like occlusions, changes in geometrical features of things, and so forth, are still to be addressed. Considering the complexity of scenarios in public places and the variety of objects, a context‐aware model is developed based on mask region‐based convolution network (M‐RCNN) for detecting abandoned objects. A modified convolution operation is implemented in the Backbone network to understand features from geometric variations near objects. These modified operation layers can be adapted based on geometric interpretations to extract required features. Finally, a bounding box operation is performed to locate the abandoned object and mask the particular thing. Experiments have been performed on the benchmark dataset like ABODA and our dataset, which shows that an mAP of 0. 0.699 is achieved for model 1, 0.675 is achieved for model 2, and 0.734 mAP is completed for model 3. An ablation analysis has also been performed and compared with other state‐of‐the‐art methods. Based on the results, the proposed model better detects abandoned objects than existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for "transfer learning" in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD. [ABSTRACT FROM AUTHOR]

The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder's intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks. [ABSTRACT FROM AUTHOR]