The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few. Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT). The IoMT has an important role to play in reducing the mortality rate by the early detection of disease. The prediction of heart disease is a key issue in the analysis of clinical dataset. The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques. Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low. Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed. The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm. The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima. The learning parameters are optimized utilizing MSSO to provide better results for ANFIS. The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc. The heart condition is identified by classifying the received sensor data using MSSO-ANFIS. A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction. The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches. The proposed MSSO-ANFIS prediction model obtains an accuracy of 99.45 with a precision of 96.54, which is higher than the other approaches.

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).

Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.

Violence detection holds immense significance in ensuring public safety, security, and law enforcement in various domains. With the increasing availability of video data from surveillance cameras and social media platforms, the need for accurate and efficient violence detection algorithms has become paramount. Automated violence detection systems can aid law enforcement agencies in identifying and responding to violent incidents promptly, thereby preventing potential threats and ensuring public protection. This research focuses on violence detection in large video databases, proposing two keyframe-based models named DeepkeyFrm and AreaDiffKey. The keyframes selection process is critical in violence detection systems, as it reduces computational complexity and enhances accuracy. EvoKeyNet and KFCRNet are the proposed classification models that leverage feature extraction from optimal keyframes. EvoKeyNet utilizes an evolutionary algorithm to select optimal feature attributes, while KFCRNet employs an ensemble of LSTM, Bi-LSTM, and GRU models with a voting scheme. Our key contributions include the development of efficient keyframes selection methods and classification models, addressing the challenge of violence detection in dynamic surveillance scenarios. The proposed models outperform existing methods in terms of accuracy and computational efficiency, with accuracy results as follows: 98.98% (Hockey Fight), 99.29% (Violent Flow), 99% (RLVS), 91% (UCF-Crime), and 91% (ShanghaiTech). The ANOVA and Tukey tests were performed to validate the statistical significance of the differences among all models. The proposed approaches, supported by the statistical tests, pave the way for more effective violence detection systems, holding immense promise for a safer and secure future. As violence detection technology continues to evolve, our research stands as a crucial stepping stone towards achieving improved public safety and security in the face of dynamic challenges.

The infrastructureless architecture for Internet-of-Drones (IoD) environment regulates drones in airspace for performing tactical tasks. Synergy is crucial for IoD participants; otherwise, complex task completion is impossible because IoD is a newer area that has gained more attention for domestic and commercial usage. However, wireless communication in the IoD environment is prone to numerous threats; security and privacy are among the top challenges. If all the participating entities of IoD become securely authenticated, then information broadcasting will never be confronted by a strong adversary. Therefore, in this research article, we designed a robust and lightweight security mechanism based on a fuzzy extractor and the MD5 (Message Digest 5) method to authenticate all IoD participants and ensure secure communication. The security analysis of the proposed biometric-based authentication mechanism has been formally verified through a simulation toolkit ProVerif and Random-Oracle Model (ROM) and informally through propositions. The performance metrics of the proposed protocol have been measured by considering communication and computation costs. The result obtained from the security and performance analysis sections shows that the secrecy, reachability, and confidentiality of the session secret key are, of course, ensuring secure information communication for the IoD environment. When comparing it with prior works, the result demonstrated that it can be strongly recommended for practical implementation in the IoD environment.

Sensing devices, high-performance networking, and privacy preservation algorithms have an important role to play in remotely sensed environmental data in smart cities. The data generated by these sensors are heterogeneous, vast, and sensitive. Therefore, it is imperative that adequate security mechanisms are put in place to protect environmental data from privacy breaches and malicious attacks. Prior to data analysis, remotely sensed environmental data, such as weather conditions (windy, cloudy, or rainy), soil types, and other similar data, must be protected. The biggest risks of remotely connected devices are that sensitive information could be leaked and devices could be compromised. Considering these security threats, this paper proposes a Pertinent Privacy Preserving Scheme (P3S). The presented scheme is reliable for sensitive geo-sensed data in thwarting the aforementioned security issues. The data is concealed using two-factor authentication from the transmitter end. In this authentication, the device and receiver's signature are overlapped for improved authentication. The failure in overlapping is identified by delayed signing time and non-coherent agreements. This identification is recurrently analyzed using federated learning. Therefore, the signing process is paused until the device verification is performed. Hence, if the device verification succeeds, then a new data privacy accumulation session is introduced. Contrarily, the accumulation is dropped, preventing compromised actual data from preserving accuracy. In two-factor authentication, lightweight digital signing cryptography is utilized. The proposed scheme maximizes the average authentication success rate and average overlapping factor by 8.86%, and 12.20% respectively. This scheme further reduces average authentication time, false data, and verification time by 10.14%, 9.70%, and 10.19% respectively.