Schizophrenia is a serious mental illness that causes delusions, overthinking, and hallucination that affects people all around the world and is the lifetime risk of suicide. Schizophrenia detection by Neurologists is quite time-consuming at the same time an expensive process, therefore researchers introduced deep learning approaches for effective detection. However, existing methods are not successful due to inaccurate outcomes, computational complexity, and suffered from overfitting issues. Such constraints are addressed by proposing a stochastic guided foraging optimized distributed activation enabled statistical multi-head attention based bidirectional long short-term memory model (StGO-DASBTM) model for schizophrenia detection. As the name represents, the model combines the StGO algorithm, statistical multi-head attention, and distribution concept to increase the efficacy of the model. Utilizing of StGO algorithm, hyper-parameters of the proposed model are tuned thereby boosting the convergence and minimizing error occurrence during the training process. Furthermore, the distributed concept reduces the computational complexity whereas, the statistical multi-head attention minimizes noise by concentrating on the relevant features of electroencephalography (EEG). As a result, the StGO-DASBTM model attains 96.42% specificity, 96.39% sensitivity, 96.40% accuracy, and 4.14 mean square error using the EEG Records Database compared to other conventional methods.

In the modern era, blockchain technology integrated with the Internet of Things (IoT) has emerged as a powerful segment for secured and translucent smart city applications. Initially, authentication forms the stepping stone for defense in different types of information systems; earlier approaches used in the context of single-side centralization were found faint and uncertain, with the enlarged single-point failure owing to external vulnerabilities. With the incorporation of an advanced authentication scheme, this research proposes the Elliptic Key-modified Rivest Shamir Adleman (EKMRS) scheme to overcome the tackles in existing techniques, thereby improving the security of applications. Moreover, the risks involved in identity fraud can be effectively minimized by blockchain technology that assures that public keys are verified using a decentralized consensus technique and stored securely. This combination not only secures communication channels but also includes a decentralized ledger for identity verification with tamper-proof evidence. The EKMRS utilizes the Elliptic Curve Digital Signature Algorithm that enhances the scalability enlargement and robust solution for a smart city environment, ensuring strong authentication. The experimental results demonstrate the effectiveness of the EKMRS scheme by offering significant improvements in terms of metrics, achieving 0.029 ms for decryption time, an encryption time of 0.39 ms, and an information loss of 0.13 with the students mark sheet dataset over the other recognized approaches.

Parkinson’s Disease (PD) functions as a continuous neurodegenerative medical condition that develops motor complications of tremors, rigidity, and slowness of movement, as well as cognitive and autonomic conditions. The correct early identification of symptoms remains essential to slow disease worsening and enhance treatment results for patients. DL and ML innovations have enabled MRI diagnosis of PD to reach new levels of improvement. This research creates a diagnostic model that unites the Hierarchical Variational Transformers (H-ViT) network for extracting features and the XGBoost classifier by combining gradient boosting and attention mechanisms to boost PD diagnostic performance. The researchers applied the framework through three neuroimaging databases, including PPMI, OpenNeuro and ADNI. When evaluated on the PPMI dataset, the combination of H-ViT and XGBoost delivered 94.5

The disease schizophrenia is a highly complex mental disorder, making it difficult to diagnose reliably due to heterogeneity and subjective features of its symptoms, many overlapping with other disorders, which makes misdiagnosis possible. Traditional diagnosis includes methods such as the use of EEG signal, where regular methods often do not succeed in capturing subtle abnormalities and to handle data variability in the process. This paper hence endeavors to overcome the mentioned challenges by introducing a Distributed Activation Function-Based Statistical Attention Bidirectional Long Short-Term Memory model for schizophrenia detection using an analysis of EEG signals. The techniques used in detecting the incidence of schizophrenia, level of complexity, and comparison of the techniques are identified as well as research gaps present in relation to the study have also been discussed. It sheds light on the DA-SA-BiLSTM model. It also talks about the strengths of the model and provides a future perspective in the area of schizophrenia diagnosis.

In this research, we addressed the recurring challenges of securing IoT networks against emerging cyber security threats. Taking advantage of the complementary strengths of Random Forest (RF) for feature selection and Bidirectional Long Short-Term Memory (BiLSTM) networks for sequential learning; we developed a novel Hybrid RF-BiLSTM model that combines feature level insights with temporal pattern recognition to provide a reliable solution for IoT traffic threats. We conducted extensive experiments with Aposemat IoT-23 dataset, where we used equal volumes of benign and malicious traffic samples leading to balanced evaluation. Furthermore, the Hybrid RF-BiLSTM model achieved a performance of 99.87%, while the Random Forest and BiLSTM performance were 99.37% and 93.32%, respectively, demonstrating the power of the hybrid approach over individual ones. The analysis gave more details about the model's performance, showing the confusion matrix and calculating the performance metrics that substantiates the model's reliability to minimize false positive and false negatives while also achieving high precision and recall. It shows that how well the integration of feature selection and sequential learning works for IoT cyber security. This Hybrid RF-BiLSTM approach lays a scalable and practical framework for real-world IoT security problems and a stepping stone for future studies in hybrid ML models for anomaly detection and threat analysis.

Parkinson’s disease (PD) traits affect millions of people the world over, with the highest prevalence in persons 50 or older. However, technical improvements were not accompanied by successful early detection of PD, making it necessary to assist clinicians in making an accurate initial diagnosis using ML-based automated methods. The objective of this study is to review in a detailed manner and compare systematically the most recent computational intelligence approaches for detection of Parkinson’s disease (PD). However, gold standard detection of PD has become largely dependent upon classification, enabling possible time savings and treatment efficiency improvements. Review of previous studies points out that there undoubtedly exists an assortment of the classification algorithms utilized in order to increase diagnose accuracy. Yet, we are still unable to identify the best classifier for PD. Therefore, as ML approaches have been employed to categorize PD patients as separate from healthy individuals or those with equivalent symptoms (i.e., other movement disorders or Parkinsonian syndromes), tools of the trade for searching the pathology–phenotype space through the lens of the phenotype (brain image) have been developed. We highlight the enormous potential of these techniques to enable a more systematic and informed PD diagnosis and assessment.

Diabetes Mellitus is a significant world health and early detection is of paramount significance since it decreases the complications and enables medical intervention in time. The paper is a comparison between the predictive accuracy of the eight Machine Learning classifiers: Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, Naive Bayes, k-Nearest Neighbors (k-NN), and an Ensemble model on the Pima Indian Diabetes dataset and a collection of clinical-biological patient records. Performance evaluation was conducted using Precision, Recall, F1-Score, and the Area Under the ROC Curve (AUC-ROC). The findings show that a significant difference was observed among the models, with SVM (AUC-ROC: 0.8648) and the Logistic Regression (AUC-ROC: 0.8638) having the best discriminative ability. A comparable study found that Logistic Regression had the highest Precision (0.7632), indicating fewer false-positive predictions, whereas Decision Tree had the highest Recall (0.7447), indicating greater sensitivity in detecting diabetes cases. The ensemble learning produced the best overall performance (AUC-ROC: 0.8709), suggesting that combining predictions from multiple models increases reliability and generalization. On the other hand, k-NN performed worst due to sensitivity to noise and the number of features. In general, the results provide evidence of the high potential of linear-margin and ensemble-based models to structured clinical data and would be a robust foundation of clinical decision support systems, which further help to broaden the role of ML-based analytics in early diabetes diagnosis and preventive health care planning.

To make better decisions, Smart Cities have embraced IoT & AI. As an outcome of the combination of IoT devices, which collect & transmit huge sets of data, and AI approaches, which process & examine this information, organizations have gained real-time insights and information to drive better decisions. With IoT and AI, decision-making processes can be automated, for instance, in predictive maintenance and supply chain management, which can improve operational efficiency, personalize the customer experience, reduce energy consumption, and enhance security. In this paper, we have discussed the rapidly identifying trends, patterns, and potential issues, IoT and AI which have transformed the decision-making process in Smart Cities and is now considered a key tool in staying ahead in today's data-driven world. We have also proposed a model which can help enhance the security concerns in Smart Cities as there has been a significant impact of IoT and AI on decision-making. In the future, it will only become more critical as technology advances and more data is generated. Overall embracing both IoT and artificial intelligence will be well-positioned for success in the future. This review article will examine the part of IoT and AI in decision-making, its benefits and challenges, and its future angle.

Schizophrenia is an acute mental illness which is difficult to diagnose often because of the similarity in its symptoms with other mental health conditions. The personal assessment of the individual is the foundation of current diagnosis method which frequently results in incorrect diagnoses and delays in discovery. The detection of schizophrenia may benefit from the application of methods based on deep learning which have advanced to the point that they can handle intricate, large amounts of data. This paper highlights the various deep learning and machine learning methods used in schizophrenia detection until now. Our focus lies on the performance metrics, shortcomings and overall status of the disease detection in the current time. In order to assist researchers and individuals working in this field, this study aims to present an in-depth analysis of schizophrenia detection.

Schizophrenia detection involves identifying the schizophrenia by analyzing specific patterns in Electroencephalogram (EEG) signals, which reflect brain activity associated with symptoms, like hallucinations and cognitive impairments. Existing models face challenges due to the complex and variable nature of EEG data, which may struggle to accurately capture critical temporal dependencies and relevant features. Traditional approaches often lack adaptability, limiting their ability to differentiate schizophrenia patterns from other brain activities. Hence, a Distributed Activation function-based statistical Attention Bi-LSTM (DA-SA-BiLSTM) is proposed for schizophrenia detection, which enhances the precision and interpretability of EEG signal analysis. This model effectively manages the temporal dependencies for the detection as it incorporates past and future data context to improve decision-making. By dynamically weighting features based on their relevance, the model emphasizes critical segments and reduces noise, increasing predictive accuracy. Using different activation functions in various layers, the DA-AB-LSTM is allowed to adapt to specific characteristics of the EEG data, strengthening its flexibility and pattern recognition abilities. Furthermore, this model refines relationships between features, facilitating precise class probability distribution for schizophrenia classification. In particular, the DA-SA-BiLSTM model outperforms the existing models with 95.9 % accuracy, the lowest mean square error (MSE) of 5.86, 95.84 % sensitivity, and 95.97 % specificity.

The recent technologies like artificial intelligence and cloud services are rapidly expanding, and virtualized data centers are gaining favor as a practical infrastructure for the telecommunications sector. End customers, including numerous private and public organizations, have widely adopted and used infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Security remains the primary issue in cloud computing systems despite its widespread acceptance. Customers of cloud services live in constant worry of availability problems, information theft, security breaches, and data loss. With the development of machine learning (ML) tools, security applications are currently becoming more and more prominent in the literature. In this study, we investigate the applicability of enhanced artificial neural networks (ANN), a well-known ML method, to identify intrusions or unusual behavior in the cloud environment. We have designed machine learning (ML) models using an enhanced ANN approach and compared the results. The UNSW-NB-15 dataset was used to train and test the models. To reduce the complexity and training time of the ML model, we have also conducted feature engineering and parameter tuning to identify the best set of features with the highest level of accuracy. We see that the accuracy of anomaly detection achieved by an enhanced ANN approach with the right features set is 99.91

In the realm of cybersecurity and network security, the significance of monitoring and analyzing network traffic cannot be overstated Network packet sniffing, a technique employed to capture and inspect data packets traversing a network, has emerged as an imperative tool for understanding network behavior, identifying vulnerabilities, and enhancing overall security. This study analyzes packet sniffing by exploring its role in modern cybersecurity practices. By analyzing packet sniffing mechanisms, including its methodologies and potential applications, this res earch sheds light on its effectiveness as a proactive security solution. Furthermore, the study examines the ethical considerations surrounding packet sniffing, addressing concerns related to user privacy and data protection. It also investigates the legal aspects that govern the use of packet sniffing techniques, ensuring compliance with regulations while maximizing its benefits. Through case studies and real-world examples, this research illustrates how packet sniffing can be leveraged to detect network anomalies, thwart malicious activities, and fortify network infrastructures. By presenting a comprehensive overview of packet sniffing's capabilities and limitations, this study equips cybersecurity practitioners with the knowledge needed to make informed decisions about its integration into their defensive strategies.

A smart city combines and oversees its social, commercial, & physical infrastructures with the use of technology in order to maximize resource usage while offering improved services to its citizens. The emergence of innovations like cloud computing, interconnected systems, & the IoT has made it possible for smart cities to provide creative solutions as well as increased direct communication and cooperation among the local administration and the populace. Among the various IoT applications, AI and block chain are the most prevalent techniques. Blockchain technique is able to present a distributed and decentralized model for IoT applications, while AI is able to provide evaluation and processing of information for IoT systems. With the use of emerging technologies, the smart city is able to become a smart society in the digital era. The rapid adoption of blockchain technique has created a new ecosystem for digital smart cities. Blockchain technique and AI are revolutionizing smart city architecture to create sustainable ecosystems. As technology advances, smart cities offer both facilities and issues when it comes to achieving our aims. This research looks at the security weaknesses that could arise when blockchain technology is used with all the devices connected in a smart city. This work also provides a detailed discussion on blockchain security issues in smart cities. Digital change presents numerous information safety and confidentiality difficulties, while its many possible advantages. To address these challenges, In order to create a safe communication system in a smart city, this study suggests a security structure that combines blockchain computing with intelligent devices. By leveraging the benefits of blockchain, such as decentralization and transparency, this framework aims to mitigate the security risks associated with smart city systems, making them more secure and resilient.

Cloud computing is rapidly expanding, and virtualized data centers are gaining favor as a practical infrastructure for the telecommunications sector. End customers, including numerous private and public organizations, have widely adopted and used Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Security remains the primary issue in cloud computing systems despite its widespread acceptance. Customers of cloud services live in constant worry of availability problems, information theft, security breaches, and data loss. With the development of machine learning (ML) tools, security applications are currently becoming more and more prominent in the literature. In this study, we investigate the applicability of enhanced Artificial Neural Networks, a well-known ML method, to identify intrusions or unusual behavior in the cloud environment. We have designed machine learning (ML) models using an enhanced ANN approach and compared the results. The UNSW-NB-15 dataset was used to train and test the models. To reduce the complexity and training time of the ML model, we have also conducted feature engineering and parameter tuning to identify the best set of features with the highest level of accuracy. We see that the accuracy of anomaly detection achieved by an enhanced ANN approach with the right features set is 99.91%. This accuracy is greater than that seen in the literature and requires fewer features to train the model.

Heart disease or Cardiovascular illness is the most prevalent cause of mortality globally. The challenge of predicting heart illness using clinical data analytics is considerable. Machine learning (ML) has been extensively used in the medical domain for disease prediction. This work performs a comparative analysis of various oversampling methods like the Synthetic Minority Oversampling technique (SMOTE), Synthetic Minority Oversampling Technique with Edited Nearest Neighbor (SMOTE-ENN), and Adaptive Synthetic Sampling Approach (ADASYN) Algorithm used with ML classifiers on imbalanced heart failure prediction dataset. Six ML classifiers are analyzed in the study Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). The accuracy metric is used to measure the model’s performance The result depicts that the ADASYN technique performs better for the given dataset and increases the accuracy of the classifier in heart failure prediction.

Heart disease, also called cardiovascular disease, is considered one of the deadliest diseases that cause high mortality worldwide. Early detection or prediction is a challenging task in the medical field. There is a massive amount of data in the healthcare industry, and processing this amount of data is a tedious task. A computer-aided system that predicts cardiac disease can save time and money. Researchers have researched several computer-assisted diagnoses for disease prediction and prognosis. In this paper, the authors provide an extensive literature survey of various classification approaches such as Machine Learning, Feature Selection, Hybrid, Ensemble, and Deep Learning used by researchers in the last decade for Heart Disease prediction. Furthermore, as the paper focuses on Machine Learning techniques, comparative analysis of the performance and accuracy of various Machine Learning techniques are summarized in tabular form. Additionally, this work critically assesses earlier methods and outlines their shortcomings. Finally, the article offers some potential future research direction in machine learning-based automated heart disease prediction.

Small and medium-sized enterprises (SMEs) are vital drivers of economic growth and champions of innovation in today’s highly connected digital environment. However, their limited resources and size make them vulnerable to the cybercriminals who seek to find ways of compromising their cybersecurity defenses. As such, by embracing various technologies that will enhance their competitive edge; SMEs expose themselves increasingly to growing threats on the Internet via sophisticated methods for attacking them online. This study examines the specific cyberspace issues confronting SMEs while suggesting opportunities and best practices for making companies’ safety more grounded. In terms of cybersecurity, these companies face unique barriers which make them open to vulnerabilities. The present study endeavors to bridge the existing knowledge gap in cybersecurity among small and medium-sized enterprises (SMEs), advocating for the implementation of best practices that safeguard businesses in the rapidly evolving and highly interconnected digital realm. The objective of this research is to identify and develop effective strategies enabling small businesses to establish a cost-effective cybersecurity ecosystem, drawing lessons from current challenges. Ultimately, the goal is to bolster the cybersecurity resilience of SMEs, ensuring their secure existence within the digital landscape.