Abstract: Cybersecurity has turn out to be one of the most critical challenges of the digital technology, as evolving assault vectors together with zero-day exploits, polymorphic malware, phishing, and IoT-based intrusions keep bypassing conventional protection systems. Deep getting to know (DL), a subset of artificial intelligence, has emerged as a effective paradigm able to learn complex styles, reading massive-scale information streams, and adapting to new chance environments with unprecedented accuracy. The motive of this evaluate is to systematically have a look at current advances in DL-primarily based cybersecurity, with a focal point on threat detection and assault mitigation across a couple of domains. This study is enormous because it consolidates fragmented studies, highlights current gaps, and identifies answers that contribute to the development of resilient cybersecurity infrastructures. A established literature evaluation of 25 peer-reviewed studies (2014–2025) became performed, synthesizing findings across application areas such as intrusion detection systems (IDS), malware analysis, phishing and fraud detection, IoT/cloud protection, opposed robustness, and go-domain employer applications. The findings show that DL models—mainly CNNs, RNNs, LSTMs, and hybrid variants—always outperform classical gadget gaining knowledge of techniques in detection accuracy and adaptableness. however, challenges which include dataset barriers, adverse vulnerability, scalability troubles, and absence of explainability persist. This assessment contributes by identifying interpretable DL, adversarially sturdy models, and federated learning as promising research guidelines. The practical implication is a shift from reactive defenses in the direction of proactive, scalable, and adaptive cybersecurity frameworks that ensure resilience in an increasing number of complicated digital ecosystems.

Keywords: Deep Learning; Cybersecurity; Intrusion Detection; Malware Detection; IoT Security; Adversarial Robustness.

Title: Deep Learning for Cybersecurity: Recent Advances in Threat Detection and Attack Mitigation- A brief Review

Author: Ifteker Hossen Rakib, MD RAJU

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 72-81

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 25-August-2025

Abstract: The rising complexity of cyber threats, consisting of AI-generated phishing and polymorphic malware, has outpaced the skills of traditional signature-based safety structures, exposing essential vulnerabilities in modern-day virtual infrastructure. This assessment significantly examines and synthesizes 24 peer-reviewed research posted between 2014 and 2024 to evaluate the transformative function of system getting to know (ML) in bridging this cybersecurity gap. The evaluation indicates that ML provides adaptive, actual-time defense mechanisms, improving accuracy in intrusion detection, malware classification, and phishing prevention. notwithstanding these advantages, demanding situations remain, such as restrained datasets, susceptibility to antagonistic attacks, and the opaque “black container” nature of complicated models, which constrain sensible deployment. The look at concludes that ML is an critical foundation for destiny cybersecurity, yet its complete capacity relies upon on studies targeted on Explainable AI (XAI), hostile resilience, and efficient set of rules layout. almost, these findings underscore the significance of interdisciplinary collaboration to translate ML-pushed intelligence from theoretical frameworks into powerful, resilient, and trustworthy cybersecurity structures.

Keywords: Machine Learning; Cybersecurity; Intrusion Detection; Adversarial Attacks; Explainable AI (XAI); Threat Detection.

Title: The Role of Machine Learning in Cybersecurity Practice: A Narrative Review

Author: Ifteker Hossen Rakib, Md Sumon Prodhan

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 65-71

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 25-August-2025

Abstract: Cassava, a major staple crop in sub-Saharan Africa, faces significant threats from leaf diseases such as Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD). Early and accurate detection is essential for timely intervention. While artificial intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), has gained traction in agricultural disease detection, inconsistencies across studies limit practical applicability. This systematic review aims to synthesize peer-reviewed research from 2020 to 2025 on AI applications for cassava disease detection using image-based methods. The review investigates model types, dataset origins, evaluation metrics, cross- comparative evaluation, and deployment readiness. A structured search was conducted across databases (IEEE Xplore, Scopus, SpringerLink, ScienceDirect, and Google Scholar), applying PRISMA guidelines. A total of 100 eligible studies were selected based on inclusion criteria. Metadata extraction covered AI methodology, dataset characteristics, performance evaluation, deployment features, and study limitations. Deep Learning dominated (65%) with CNN variants widely used, followed by ML (20%) and hybrid models (10%). PlantVillage was the most common dataset (64%), while field data accounted for only 21%. Accuracy was the most reported metric (95%), but only 20% used multi-metric evaluations. Cross-validation and cross-comparative evaluations were inconsistent. Few studies addressed deployment readiness or real-world applicability. Although AI methods have demonstrated high potential in cassava disease detection, significant gaps remain in dataset diversity, model evaluation rigor, deployment design, and ethical considerations. This review provides a road map for future research, emphasizing the need for standardized benchmarking, mobile-friendly models, and real-world field validation.

Keywords: Cassava, Deep Learning, Machine Learning, Disease Detection, Agriculture.

Title: A Systematic Review of Machine and Deep Learning Techniques for Cassava Disease Detection: Trends, Challenges, and Pathways to Real-World Implementation

Author: Chika Norah John, Obi Chukwuemeka Nwokonkwo, Anthony Ifeanyi Otuonye, Ikechukwu Ignatius Ayogu, Innocent Harvey Ajunwa

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 49-64

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 04-August-2025

Abstract: The rapid integration of artificial intelligence (AI) algorithms into decision-making processes across sectors such as employment, criminal justice, and healthcare has heightened concerns about bias and fairness. These algorithms, if not carefully designed and monitored, can perpetuate societal biases or introduce new forms of discrimination, resulting in inequitable outcomes. This research article investigates the legal standards and ethical guidelines developed to address these challenges, focusing on their role in promoting fairness in AI systems. Through a comprehensive review of key legal frameworks, notably the European Union’s AI Act, and ethical recommendations, such as UNESCO’s Recommendation on the Ethics of Artificial Intelligence, the study elucidates the mechanisms in place to mitigate bias. Findings reveal that legal standards mandate rigorous risk assessments, high data quality, and bias detection protocols, particularly for high-risk AI applications. For example, the AI Act requires providers to implement quality management systems and ensure human oversight. Ethical guidelines emphasize principles like fairness, non-discrimination, transparency, and accountability, with UNESCO advocating for inclusive AI development to promote social justice. However, significant challenges persist, including technical difficulties in measuring and mitigating bias, the need for interdisciplinary collaboration, and the rapid evolution of AI technologies. The article argues that integrating legal and ethical considerations is crucial for developing AI systems that are innovative yet equitable. It concludes that ongoing efforts are necessary to adapt and enforce these standards effectively, fostering trust and acceptance in AI technologies. By aligning legal requirements with ethical principles, stakeholders can address the complex interplay of bias and fairness, ensuring AI serves as a tool for societal benefit.

Keywords: AI bias, fairness, legal standards, ethical guidelines, non-discrimination.

Title: Bias and Fairness in AI Algorithms: Legal Standards and Ethical Guidelines

Author: Dr. Rahul Kailas Bharati

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 44-48

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 02-August-2025

La question centrale de cette recherche concerne l'évaluation des points faibles des réseaux électriques face aux dangers cybernétiques. L'objectif est de cerner les défis cruciaux en matière de cybersécurité et de suggérer des remèdes appropriés. Pour atteindre cet objectif, il est impératif de recueillir des informations sur les incidents de cybersécurité déjà survenus, les dispositifs de protection actuellement en place, et également sur la façon dont les professionnels du secteur perçoivent les menaces et les approches de prévention. Ainsi, l'étude vise à comprendre comment protéger au mieux ces infrastructures vitales [citeX].

Title: Cybersécurité des infrastructures électriques: enjeux et solutions

Author: KALOBWE KUNDA Fils, NYIRONGO BANDA Mike

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 19-43

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 29-July-2025

Abstract: The global construction industry contributes significantly to environmental degradation, accounting for 39% of carbon emissions and 36% of energy consumption. The sustainable building design and construction (SBDC) offers solutions through improved energy efficiency and reduced environmental impact, its adoption in developing nations like Nigeria remains limited. This study identifies and analyzes the key barriers to SBDC implementation in Anambra State through a survey of 322 construction professionals, including contractors, clients, and consultants. Results indicate that financial and policy-related challenges are the most critical obstacles, with high initial costs (Mean=4.52, SD=0.61), lack of government incentives (Mean=4.38, SD=0.72), and limited material availability (Mean=4.15, SD=0.83) ranking as top barriers. Technical knowledge gaps (Mean=3.94) and low stakeholder awareness (Mean=3.87) were also significant, though with greater variability in responses. Resistance to change (Mean=3.65), weak regulation enforcement (Mean=3.42), and perceived project delays (Mean=3.21) were moderately impactful, while financing limitations (Mean=3.05) and cultural preferences (Mean=2.87) were less critical but contextually relevant. The study recommends immediate policy reforms, including financial incentives like subsidies and tax breaks, alongside stricter enforcement of green building regulations. The study also recommends Capacity-building programs for professionals and public awareness campaigns as essentials to address knowledge gaps, while local production of sustainable materials could improve affordability and accessibility.

Keywords: Sustainable construction, green buildings, barriers, Nigeria, policy, financial constraints.

Title: A Comprehensive Survey of Intelligent Techniques for Ransomware Mitigation in Corporate Networks

Author: Gloria N. Ezeh, Udoka F. Eze, Baldwin C. Asiegbu, Charles O. Ikerionwu, Mathew E. Nwanga, Vivian C. Mbamala.

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 7-18

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 03-June-2025

Abstract: The global construction industry contributes significantly to environmental degradation, accounting for 39% of carbon emissions and 36% of energy consumption. The sustainable building design and construction (SBDC) offers solutions through improved energy efficiency and reduced environmental impact, its adoption in developing nations like Nigeria remains limited. This study identifies and analyzes the key barriers to SBDC implementation in Anambra State through a survey of 322 construction professionals, including contractors, clients, and consultants. Results indicate that financial and policy-related challenges are the most critical obstacles, with high initial costs (Mean=4.52, SD=0.61), lack of government incentives (Mean=4.38, SD=0.72), and limited material availability (Mean=4.15, SD=0.83) ranking as top barriers. Technical knowledge gaps (Mean=3.94) and low stakeholder awareness (Mean=3.87) were also significant, though with greater variability in responses. Resistance to change (Mean=3.65), weak regulation enforcement (Mean=3.42), and perceived project delays (Mean=3.21) were moderately impactful, while financing limitations (Mean=3.05) and cultural preferences (Mean=2.87) were less critical but contextually relevant. The study recommends immediate policy reforms, including financial incentives like subsidies and tax breaks, alongside stricter enforcement of green building regulations. The study also recommends Capacity-building programs for professionals and public awareness campaigns as essentials to address knowledge gaps, while local production of sustainable materials could improve affordability and accessibility.

Keywords: Sustainable construction, green buildings, barriers, Nigeria, policy, financial constraints.

Title: Assessment of the Barriers to the Adoption of Sustainable Building Design and Construction in Anambra State, Nigeria

Author: Obodoh D.A., Ezemenike R.C., Mohammed D., Ozoemena C.O.

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 12, Issue 2, May 2025 - August 2025

Page No: 1-6

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 10-May-2025