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In the proposed work, hidden Markov model (HMM) has been deployed to improve the learner's performance or grades on the basis of their psychological and environmental factors like connect/gather isolation, pleasure/comfort, depression, trust, anxiety, proper guidance, improper guidance, entertainment, and stress. The categorization of psychological and environmental factors has been done on the basis of two factors as positive and negative. The responsibility of the positive factor is to boost up learner's performance or grades, whereas negative factors reduce learning performance respectively. Finally, this paper addresses the application of HMM to determine the optimal sequence of states for different states as grades A, B, and C for different emission observations. The states identification leads to training the HMM model where optimal value of individual states computed using different observation sequences which determines the probability of state sequences. The probability of achieved optimal states is shown in different logical combinations where best state is searched among available different states using different search techniques. The computational results obtained after training are encouraging and useful.

Human activity recognition (HAR) is the process of interpreting human activities with the help of electronic devices such as computer and machine version technology. Humans can be explained or clarified as gestures, behavior, and activities that are recorded by sensors. In this manuscript, we concentrate on studying the problem of HAR; for this, we use the proposed theory of Aczel and Alsina, such as Aczel–Alsina (AA) norms, and the derived theory of Choquet, such as the Choquet integral in the presence of Atanassov interval-valued intuitionistic fuzzy (AIVIF) set theory for evaluating the novel concept of AIVIF Choquet integral AA averaging (AIVIFC-IAAA), AIVIF Choquet integral AA ordered averaging (AIVIFC-IAAOA), AIVIF Choquet integral AA hybrid averaging (AIVIFC-IAAHA), AIVIF Choquet integral AA geometric (AIVIFC-IAAG), AIVIF Choquet integral AA ordered geometric (AIVIFC-IAAOG), and AIVIF Choquet integral AA hybrid geometric (AIVIFC-IAAHG) operators. Many essential characteristics of the presented techniques are shown, and we also identify their properties with some results. Additionally, we take advantage of the above techniques to produce a technique to evaluate the HAR multiattribute decision-making complications. We derive a functional model for HAR problems to justify the evaluated approaches and to demonstrate their supremacy and practicality. Finally, we conduct a comparison between the proposed and prevailing techniques for the legitimacy of the invented methodologies. [ABSTRACT FROM AUTHOR]

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%. [ABSTRACT FROM AUTHOR]

Cancer data analysis is significant to detect the codes that are responsible for cancer diseases. It is significant to find out the coding regions from diseases infected biological data. The infected data will be helpful to design proper drugs and will be supportable in laboratory assessments. Codes bear specific meaning on various features as well as symptoms of diseases. Coding of biological data is a key area to get exact information on animals to discover the desired medicine. In the current work, four different machine learning approaches such as support vector machine (SVM), principal component analysis (PCA) technique, neural mapping skyline filtering (NMSF) and Fisher’s discriminant analysis (FDA) were applied for data reduction and coding area selection. The experimental analysis established that the SVM outperforms PCA and FDA. However, due to the mapping facility, NMSF outperforms SVM. Thus, the NMSF achieved the preeminent results among the four techniques. Matthews’s correlation coefficient was used to evaluate the accuracy, specificity, sensitivity, F-measures and error rate of the four methods that are used to determine the coding area. Detailed experimental analysis included comparison study among the four classifiers for the deoxyribonucleic acid dataset. [ABSTRACT FROM AUTHOR]

• To develop a new energy-efficient and secure clustering based data transmission scheme for pervasive wireless networks. • To employ red deer algorithm (RDA) based clustering technique with blockchain enabled secured data transmission. • The experimental validation of the RDAC-BC technique is assessed and the results are compared with existing methods. Energy efficiency and security are considered as important issues in the design of pervasive wireless networks. Since the nodes in pervasive wireless networks are battery-operated, it becomes essential to develop an energy-efficient method to minimize energy consumption and prolong the network lifetime. This paper presents new energy-efficient and secure clustering based data transmission in pervasive wireless networks using red deer algorithm (RDA) based clustering technique with blockchain enabled secured data transmission, named as RDAC-BC. The proposed RDAC-BC technique undergoes node initialization and performs the clustering process using the RDAC technique. The clustering technique performs cluster head (CH) selection and cluster construction process is carried out. Once the CHs are chosen, blockchain enabled secure data transmission takes place among cluster members (CMs) as well as CHs. The application of RDAC and blockchain technology helps to achieve energy efficiency and security. The experimental validation of the RDAC-BC technique is assessed under several aspects and the results are compared with existing methods. The obtained results ensured that the RDAC-BC technique has shown superior results interms of energy, network lifetime, packet delivery ratio (PDR), and throughput. [ABSTRACT FROM AUTHOR]

Lately, the Internet of Things (IoT) has opened up new opportunities to business and enterprises; however, the cost of providing security and privacy best practices is preventing numerous organizations from adopting this innovation. With the proliferation of connecting devices in IoT, significant increases have been recorded in energy use, harmful contamination and e-waste. A new paradigm of green IoT is aimed at designing environmentally friendly protocols by reducing the carbon impact and promote efficient techniques for energy use. There is a consistent effort of designing distinctive security structures to address vulnerabilities and attacks. However, most of the existing schemes are not energy efficient. To bridge the gap, we propose the hybrid logical security framework (HLSF), which offers authentication and data confidentiality in IoT. HLSF uses a lightweight cryptographic mechanism for unique authentication. It enhances the level of security and provides better network functionalities using energy-efficient schemes. With extensive simulation, we compare HLSF with two existing popular security schemes, namely, constrained application protocol (CoAP) and object security architecture for IoT (OSCAR). The result shows that HLSF outperforms CoAP and OSCAR in terms of throughput with low computational, storage and energy overhead, even in the presence of attackers. [ABSTRACT FROM AUTHOR]

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%., Competing Interests: The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript., (© 2020 Informa UK Limited, trading as Taylor & Francis Group.)