Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.

In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte. Consequently, security issues have also been arisen with the network growth. However, intrusion detection in such big data is challenging. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks’ exposure to security and privacy threats. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Conventional techniques are not effective enough to cope the advanced attacks. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. In this review, public network-based datasets of IDS are fully explored and analyzed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed.

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one’s health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121µW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power.

The application area of technology is expanding the span of information size is also additionally increases. Classification gets to be troublesome in view of unbounded size and imbalance nature of data. Class imbalance where one of the two classes having more sample than other years. There are typical strategies for an imbalance data set which is zoned into three main categories, the algorithmic methodology, data preprocessing approach and feature selection approach. In this paper every methodology is characterize which gives the right bearing for exploration in the class imbalance problem. This Paper also examines the three basic divisions of class Imbalance learning like data-preprocessing, the algorithmic approach, and feature selection approach.

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

IoT-based multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification using sensors. The leakage of the biometric trait information may cause critical privacy and security issues. It is expected to protect the privacy details of individuals through the irreversibility, unlinkability, and renewability of multi-biometric templates used in the authentication system. This study presents a robust authentication system with secure multi-biometric template protection techniques based on discrete cosine transform feature transformation and Lagrange’s interpolation-based image transformation. Three biometric traits namely iris, fingerprint, and palm print are recorded using sensors to validate the proposed multi-biometric template protection system. The fusion of all traits used is giving an average of 95.42% genuine acceptance rate and an average of 4.57% false rejection rate. Despite any number of biometric templates used for authentication, the proposed image transformation techniques keep the size of the final storage requirement as 8 X 8, which achieves constant space complexity (O(1)). The stored template is not linked with original templates; it is irreversible and renewable as new enrolment of the same individual will produce a new template every time. Overall, the proposed technique provides a secure authentication system with high accuracy, a constant size database, and the privacy preservation of biometric traits.

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The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining “Term Frequency (TF)/Inverse Document Frequency (IDF),” “Bag of Words,” and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.