AlKadhim Journal for Computer Science

LV2PA: A Lightweight Verification with Privacy-Preserving Authentication for Vehicular Communications

Murtadha Alazzawi — 2025-06-25

Security and privacy must be taken into account for vehicular ad-hoc networks (VANETs) due to the fact that broadcasting occurs through an open communication channel. This work offers a Lightweight Verification with Privacy-Preserving Authentication (LV2PA) approach for vehicular communications to overcome these challenges. To satisfy security and privacy requirements, the proposed LV2PA approach employs not only the cryptographic hash function, but also a Bloom filter and the Chinese remainder theorem. During the mutual authentication of the LV2PA scheme, only the first roadside unit (RSU) and on-board unit are required to communicate with a trusted authority (TA) due to the changeover use, however the other RSUs in vehicular communications do not require TA communication. Consequently, bottleneck problems for the TA are avoided. In addition, the RSU updates the shared group key whenever a vehicle joins or departs the group; hence, the proposed LV2PA provides complete forward secrecy and backward secrecy for vehicular communications. The formal (Burrows–Abadi–Needham (BAN) logic) and informal security analyses demonstrate that the proposed LV2PA scheme is legitimate and meets the security and privacy requirements, respectively. In terms of computing and communication expenses, the performance evaluation of the proposed LV2PA scheme has advantageously low overhead and low latency compared to state-of-the-art schemes

Automated Emotion Recognition Using Hybrid CNN-RNN Models on Multimodal Physiological Signals.

AMMAR AZEEZ — 2025-06-25

Emotion recognition has emerged as one of the cornerstones of human-computer interaction, thus opening new frontiers in healthcare, education, and entertainment. The ability to automate emotion recognition processes using hybrid Convolutional Neural Network-Recurrent Neural Network models offers a promising avenue for decoding complex emotional states. The proposed study develops an approach for the integration of electrocardiogram, galvanic skin response, and facial expressions for performing emotion recognition in an accurate and efficient manner. This hybrid architecture combines the strengths of CNNs in spatial feature extraction and RNNs in modeling temporal dependencies, which naturally provides a remedy for challenges inherently brought about by the use of multimodal data. Extensive experiments have been conducted on benchmark datasets publicly available, and the proposed hybrid model outperforms other unmoral and traditional methods in terms of higher classification accuracy and robustness. This study points not only to the potential of hybrid models in advancing emotion recognition but also provides a scalable framework adaptable for real-world applications such as mental health monitoring and adaptive learning systems. The results underlined how deep learning techniques can dramatically bridge the gap between subjective emotional experiences and objective computational analyses.

Fake News Detection: A Comprehensive Taxonomy of Text, Image, Video, and Multi-Modal Techniques

Hussein Al-Kaabi — 2025-06-25

The widespread dissemination of fake news across digital platforms has posed significant challenges for information integrity, social stability, and public trust. Traditional fake news detection approaches, primarily based on text analysis, are no longer sufficient, as misinformation now integrates multi-modal content, including images, videos, and manipulated metadata. This paper presents a comprehensive taxonomy of fake news detection techniques, categorizing existing methods into text-based, image-based, video-based, and multi-modal approaches. We review the evolution of detection methodologies, from traditional machine learning models to advanced deep learning architectures, including transformers, convolutional neural networks (CNN), and hybrid AI models. Additionally, we analyze the growing challenge of adversarial attacks, where malicious actors manipulate text, images, and videos to bypass detection systems. Finally, we highlight emerging research directions, such as adversarial-resilient AI models, cross-modal fact verification, and human-AI hybrid fact-checking systems, which are crucial for developing trustworthy, explainable, and robust fake news detection frameworks. This study serves as a foundation for researchers and practitioners in advancing multi-modal misinformation detection and strengthening AI-driven fact-checking mechanisms.

Security Content Used To Protect Data From Social Media

Zina Alshukri — 2025-06-25

Abstract

Social media networks have revolutionized global connectivity, enabling billions of users to engage in virtual communities and mutual interactions. However, their widespread adoption has attracted malicious actors who exploit platform vulnerabilities to compromise user security and privacy. Despite preventive measures, cyber-attacks targeting social media have surged, necessitating advanced intrusion detection systems (IDS) to mitigate risks. Although these platforms fetch never-seen convenience, users do not have the technical ability to understand the privacy implications of their shared content. As a result the use of available privacy settings fall short compared to the general practice of security. This study initiates the development of a holistic framework for social media security that integrates

Policy-driven safeguards: Use strong passwords, keep updating your credentials often, share data carefully, use antivirus software, and stick to your own software.
Artificial Intelligence (AI) and Machine Learning (ML)-driven solutions:: Machine learning algorithms for user sentiment analysis, disinformation detection, combating illicit activities such as child trafficking, and adversarial machine learning-based enhancement of intrusion detection.
Ethical AI integration: Aligning with "AI for Good" efforts can help cut down biases and ensure fairness in automated security setups.

The paper takes a close look at the latest improvements in social media security, stressing how important it is to protect private information as more breaches happen that could harm economic stability and confidence in using these platforms. This helps connect tech creativity with strict rules and offers a new way to strengthen platform trustworthiness and ability to bounce back in a digital world that is becoming more competitive.

Quantitative Assessment of Energy Storage Systems For Enhanced Utilization of Renewable Energy in Agricultural Setting

Ahmed Majbel — 2025-06-25

This study presents a comprehensive quantitative assessment of Energy Storage Systems (ESS) to enhance the integration of renewable energy in agricultural settings. Given the intermittent nature of solar and wind energy, ESS offers a strategic solution to ensure energy reliability for critical farming operations such as irrigation, processing, and cold storage. The research employs dynamic simulation models, field data from Egypt, India, and Brazil, and techno-economic analysis to compare battery, thermal, and hydropower storage technologies. Key performance indicators—including energy efficiency, Levelized Cost of Storage (LCOS), carbon reduction, and user satisfaction—are evaluated across diverse agro-climatic zones. Results reveal that no single ESS is universally optimal; each technology has trade-offs based on climate, cost, and scalability. The study highlights the importance of climate-specific system design, targeted subsidies, and capacity-building initiatives to support the adoption of sustainable energy solutions in agriculture. Findings aim to guide policymakers, engineers, and farmers toward resilient, low-emission agricultural energy systems.

A A Comprehensive Review of Intrusion Detection Systems in IoT networks Using ML and DL Techniques

fatima Rahim — 2025-06-25

The Internet of Things (IoT) is growing at an extremely rapid rate, impacting all aspects of our lives and extending to various fields, including wearable technology, smart sensors, and home appliances. However, the rapid growth is coupled with serious security concerns that render these technologies vulnerable to hacking opportunities and erode user privacy, as well as data protection, especially as cyber-attacks become more complex. Intrusion detection is a crucial aspect for tracking and thwarting such attacks. Machine learning (ML) and deep learning (DL) algorithms have ever-increasing efficiency in automating procedures like these. This study aims to provide researchers with a comprehensive overview of contemporary Intrusion Detection System (IDS) techniques employed in the IoT environment, highlighting strengths and weaknesses. It also gives direction to future research by suggesting that more adaptive, lightweight, and efficient intrusion detection systems can be developed to address the unique constraints of IoT networks.