Stress Level Prediction and Monitoring Using CNN Model
Secure Multi-Spectral Image Encryption Using Chaos and Gravitational Diffusion
Security Challenges in Smart IoT Systems and Their Solutions
Salesforce Classic as well as Lightning automation using TOSCA Automation and TOSCA AI-powered salesforce Engine
Beyond the Hook: Advanced Phishing Techniques And AI-Driven Defenses
Stress at work has become a serious problem that affects worker health and business success. Traditional methods of measuring stress, such as self-reports and surveys, are un- reliable and may not provide immediate feedback. To overcome these problems, this paper proposes a real-time stress monitoring system that analyzes facial expressions and detects stress using CNNs. The system is suitable for modern workplaces because it uses MobileNetV2 for fast and scalable processing. It also features a chatbot powered by an artificial neural network (ANN) that provides customized stress reduction recommendations, including relaxation techniques and counseling materials. Based on the pilot test results, the system is accurate and efficient, making it a useful tool for managing stress in different work settings. I
This study introduces an advanced encryption technique for multi-spectral images that combines chaotic systems and a gravitational model to enhance security. The method tackles challenges like high dimensionality and inter-band correlations through a multi-layered approach. By using bit-plane decomposition, it achieves precise data manipulation, while a hybrid chaotic system [2D Logistic-Tent-Modulated Map and 1D Sine-Cosine-Sawtooth Map] ensures high-quality randomness for pixel and spectral band scrambling. Additionally, a gravitational model-based diffusion process dynamically modifies pixel intensities, further strengthening encryption, dynamic image-dependent key generation ensures unique encryption keys for every image, enhancing resistance to brute-force attacks. The decryption process is fully reversible, ensuring accurate image reconstruction. Experimental results highlight the method's high sensitivity to initial conditions, strong defence against statistical and differential attacks, and efficient handling of multi-spectral data. This makes it a secure and scalable solution for applications like remote sensing, medical imaging, and defence.
The rapid proliferation of Smart Internet of Things (IoT) systems across various sectors, including healthcare, transportation, and smart homes, has significantly transformed the way we interact with technology. However, this surge in connectivity has also introduced a myriad of security challenges that pose serious risks to users and organizations alike. This research paper aims to identify and analyze the key security vulnerabilities inherent in Smart IoT systems and propose comprehensive solutions to mitigate these risks. The study begins by examining the diverse landscape of IoT devices (Yogesh K Dwivedi et al., 2022), which often lack standardized security protocols, making them susceptible to various cyber threats such as malware, unauthorized access, and denial-of-service attacks. Many devices are shipped with default passwords that remain unchanged, further exacerbating the security dilemma. The paper highlights the critical need for robust security measures, emphasizing the importance of encryption, secure boot processes, and regular software updates (Tataria H et al., 2021. Furthermore, the research advocates for a multi-layered security approach that involves collaboration among manufacturers, users, and regulatory bodies. By establishing industry-wide standards and promoting user awareness regarding security best practices, the overall resilience of IoT systems can be significantly enhanced. The integration of advanced technologies, such as machine learning and blockchain, is also explored as a means to improve threat detection and data integrity. ((Dash S et al., 2019), (Zhou Z et al., 2019), (Spyrolari et al., 2021). In conclusion, this paper underscores the urgency of addressing IoT security challenges to foster trust and reliability in these innovative technologies. As the number of connected devices continues to grow, ongoing research and cooperative efforts are essential to develop effective strategies that ensure a secure digital future. The findings of this study aim to contribute to the evolving discourse on IoT security, providing actionable insights for stakeholders across the industry.The rapid proliferation of Smart Internet of Things (IoT) systems across various sectors, including healthcare, transportation, and smart homes, has significantly transformed the way we interact with technology. However, this surge in connectivity has also introduced a myriad of security challenges that pose serious risks to users and organizations alike. This research paper aims to identify and analyze the key security vulnerabilities inherent in Smart IoT systems and propose comprehensive solutions to mitigate these risks. The study begins by examining the diverse landscape of IoT devices (Yogesh K Dwivedi et al., 2022), which often lack standardized security protocols, making them susceptible to various cyber threats such as malware, unauthorized access, and denial-of-service attacks. Many devices are shipped with default passwords that remain unchanged, further exacerbating the security dilemma. The paper highlights the critical need for robust security measures, emphasizing the importance of encryption, secure boot processes, and regular software updates (Tataria H et al., 2021. Furthermore, the research advocates for a multi-layered security approach that involves collaboration among manufacturers, users, and regulatory bodies. By establishing industry-wide standards and promoting user awareness regarding security best practices, the overall resilience of IoT systems can be significantly enhanced. The integration of advanced technologies, such as machine learning and blockchain, is also explored as a means to improve threat detection and data integrity. ((Dash S et al., 2019), (Zhou Z et al., 2019), (Spyrolari et al., 2021). In conclusion, this paper underscores the urgency of addressing IoT security challenges to foster trust and reliability in these innovative technologies. As the number of connected devices continues to grow, ongoing research and cooperative efforts are essential to develop effective strategies that ensure a secure digital future. The findings of this study aim to contribute to the evolving discourse on IoT security, providing actionable insights for stakeholders across the industry.
This paper examines how TOSCA Automation and the TOSCA AI-driven Salesforce Engine function to enhance automation in Salesforce Classic and Lightning systems. It particularly looks at how these tools boost efficiency, accuracy, and user satisfaction in sales activities. By collecting both qualitative and quantitative data, including user surveys, performance statistics, and case studies from companies utilizing these automation tools, the research indicates notable improvements in work processes. User satisfaction increased by over 30%, and task completion time reduced by roughly 25%. These findings underscore TOSCA's effectiveness in streamlining sales workflows, not only in business contexts but also in healthcare, where proper service and data management are crucial. The research findings carry important implications for healthcare, indicating that using advanced automation tools can enhance productivity and resource management, subsequently improving patient outcomes and satisfaction levels. This study contributes to the current understanding of digital transformation in healthcare, demonstrating how robotic process automation can assist with data-intensive tasks and foster an innovative environment aligned with the healthcare sector's growing technological emphasis.
Phishing attacks are one of the most prevalent and dangerous forms of cybercrime today. These attacks exploit human psychology and technical vulnerabilities to steal sensitive information, including login credentials, financial data, and personal identification. This paper explores the anatomy of phishing attacks, common techniques employed by attackers, notable real-world cases, and advanced countermeasures using artificial intelligence (AI), machine learning (ML), and user education. A comprehensive analysis of phishing trends and defense strategies is provided to inform both technical and non-technical audiences about the importance of cybersecurity resilience.
