Alkadhim Journal for Computer Science

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

bstract
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.

The widespread dissemination of fake news across digital platforms has posed
significant challenges to information integrity, social stability, and public trust. Traditional fake news detection approaches, primarily based on text analysis, are no longer sufficient. 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. These directions are crucial for developing
trustworthy, explainable, and robust fake news detection frameworks. This
study is a foundation for researchers and practitioners to advance multi-modal
misinformation detection and strengthen AI-driven fact-checking mechanisms.

The integration of renewable energy (RE) into agriculture is critical for achieving
sustainability goals, yet its intermittent nature often fails to meet the continuous
energy demands of farming operations such as irrigation, cooling, and
processing. Motivated by the lack of comprehensive, climate-specific evaluations of
energy storage systems (ESS) in agriculture, this study develops a quantitative
framework to assess the technical, economic, and environmental viability of three
ESS technologies—lithium-ion batteries, small-scale pumped hydropower (SPHS), and thermal storage—across arid (Egypt), tropical (India), and temperate
(Netherlands) regions. Key findings reveal:  Lithium-ion batteries achieve 90% efficiency in moderate climates but
degrade to 75% under extreme heat (>40°C), underscoring the need for
climate-specific designs.  SPHS offers the lowest long-term cost (LCOS: $0.05–0.10/kWh) in
mountainous regions like Brazil, making it ideal for long-duration storage.  Thermal storage reduces greenhouse heating emissions by 70% in
controlled environments like Dutch greenhouses. The study demonstrates that tailored ESS adoption can reduce energy costs by 30–
50% and carbon emissions by 40–85%, while enhancing resilience against grid
instability. Policy insights emphasize targeted subsidies (e.g., India’s KUSUM
scheme), farmer training, and public-private partnerships to accelerate adoption. By
bridging the gap between renewable energy potential and agricultural energy
demands, this work directly advances Sustainable Development Goals (SDGs) 7
(Affordable and Clean Energy) and 13 (Climate Action), offering a roadmap for
global sustainable farming.

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
1. Policy-driven safeguards: Use strong passwords, keep
updating your credentials often, share data carefully, use
antivirus software, and stick to your own software. 2. 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. 3. 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.

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