Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporati...
详细信息
Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, a...
详细信息
Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computerscience. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri
In recent years, there has been a surge of interest in combining artificial intelligence (AI) with education to enhance learning experiences. However, one major concern is the lack of transparency in AI models, which ...
详细信息
In recent years, there has been a surge of interest in combining artificial intelligence (AI) with education to enhance learning experiences. However, one major concern is the lack of transparency in AI models, which hinders our ability to understand their decision-making processes and establish trust in their outcomes. This study aims to address these challenges by focusing on the implications of explainable and trustworthy AI in education. The primary objective of this research is to improve trust and acceptance of AI systems in education by providing comprehensive explanations for model predictions. By doing so, it seeks to equip stakeholders with a better understanding of the decision-making process and increase their confidence in the outcomes. Additionally, the study highlights the importance of evaluation metrics in assessing the quality and effectiveness of explanations generated by explanation AI models. These metrics serve as vital tools for ensuring reliable system performance and upholding the fundamental principles necessary for building trustworthy AI. To accomplish these goals, the study utilizes the LBLS-467 dataset to predict high-risk students, employing both logistic regression and neural networks as AI models. Subsequently, explanation artificial intelligence techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) are utilized to evaluate students' learning outcomes and provide explanations. Finally, six evaluation indicators are adopted to assess the accuracy and stability of these explanations. In conclusion, this study addresses the challenges associated with inconsistencies in explainable AI models within the field of education. It emphasizes the need for explainability and trust when applying AI systems in educational contexts. By providing comprehensive explanations and evaluation metrics, this research empowers education teams to make informed decisions and fosters a positive envir
Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for c...
详细信息
Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucia...
详细信息
Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.
To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for ...
详细信息
To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for the authentication of data between the data user and the data owner. However, blockchain networks are vulnerable to potential privacy risks and security issues concerned with the data transfer and the logging of data transactions. To overcome these challenges and enhance the security associated with blockchain storage systems, this research develops a highly authenticated secure blockchain storage system utilizing a rider search optimized deep Convolution Neural Network(CNN) model. The architecture integrates the Ethereum blockchain, Interplanetary File System (IPFS), data users, and owners, in which the Smart contracts eliminate intermediaries, bolstering user-owner interactions. In tandem, blockchain ensures immutable transaction records, and merging IPFS with blockchain enables off-chain, distributed storage of data, with hash records on the blockchain. The research accomplishes privacy preservation through six-phase network development: system establishment, registration, encryption, token generation, testing, and decryption. Parameters for secure transactions are initialized, user registration provides genuine user transaction credentials, and encryption guarantees data security, employing optimized Elliptic Curve Cryptography (ECC). Further, the optimized ECC algorithm is developed utilizing a novel rider search optimization that utilizes search and rescue characteristics of human, and rider characteristics for determining the shorter key lengths. Token generation involves issuing digital tokens on a blockchain platform, followed by testing using a deep CNN classifier to detect anomalies and prevent unauthorized data access during the test phase. The decryption of data is conducted for registered users. The developed rider search optimized deep CN
In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "gre...
详细信息
In AI pandemic applications, the online automatic AI recording apparatus for official councils such as court trials, business conferences and commercial meetings will become imperative because it could let the opinion...
详细信息
Aim/Purpose COVID-19 was an unprecedented disruptive event that accelerated the shift to remote work and encouraged widespread adoption of digital tools in organizations. This empirical study was conducted from an org...
详细信息
Aim/Purpose COVID-19 was an unprecedented disruptive event that accelerated the shift to remote work and encouraged widespread adoption of digital tools in organizations. This empirical study was conducted from an organizational-strategic perspective, with the aim of examining how the COVID-19 pandemic outbreak affected employees’ use of organizational information systems (IS) as reflected in frequency. Background To date, only a limited effort has been made, and a rather narrow perspective has been adopted, regarding the consequences of the adoption of new work environments following COVID-19. It seems that the literature is lacking in information regarding employee use of organizational IS since the outbreak of the pandemic. Specifically, this issue has not yet been examined in relation to employees’ perception about the organization’s digital efforts and technological maturity for remote work. The present study bridges this gap. Methodology The public sector in Israel, which employs about a third of the Israeli workforce, was chosen as a case study of information-intensive organizations. During the first year of COVID-19, 716 questionnaires were completed by employees and managers belonging to four government ministries operating in Israel. The responses were statistically analyzed using a Chi-Square and Spearman’s Rho tests. Contribution Given that the global pandemic is an ongoing phenomenon and not a passing episode, the findings provide important theoretical and practical contributions. The period prior to the COVID-19 pandemic and the period of the pandemic are compared with regard to organizational IS use. Specifically, the study sheds new light on the fact that employee perceptions motivated increased IS use during an emergency. The results contribute to the developing body of empirical knowledge in the IS field in the era of digital transformation (DT). Findings More than half of the respondents who reported that they did not use IS before COVID-19 stated
Clustering strategies for reducing the energy consumption and extending the network life have been employed widely in Wireless Sensor Network (WSN). The clustering mechanism can extend the network’s service life and ...
详细信息
暂无评论