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Current Issue – Vol.6, Issue.3 (July-September 2026)

Current Issue – Vol.6, Issue.3 (July-September 2026)


Gender Perspectives in Rural Employment: The Role of Human Resource Management in Empowering Women through Self-Help Groups in Telangana

Adevally Soujanya

Research Paper | Journal Paper

Vol.6, Issue.2, pp.01-07, September-2026

DOI: 10.5281/zenodo.23138127

Abstract References Citation Download

Abstract

Women's empowerment is essential for achieving inclusive and sustainable rural development, particularly in developing countries where Self-Help Groups (SHGs) play a significant role in improving women's socio-economic conditions. The present study examines the influence of Human Resource Management (HRM) practices on the economic, social, and psychological empowerment of women participating in SHGs in the districts of Nalgonda, Suryapet, and Yadadri Bhongiri in Telangana. The study adopted a quantitative research design, and primary data were collected from 320 women SHG members using a structured questionnaire. Stratified random sampling was employed to select the respondents, and the collected data were analysed using descriptive statistics and Analysis of Variance (ANOVA). The findings reveal that HRM practices, namely Training and Development, Performance Recognition, Leadership Building, and Group Facilitation, have a statistically significant positive influence on women's empowerment. However, Conflict Resolution did not exhibit a significant relationship with the three dimensions of empowerment. The study concludes that integrating structured HRM practices into SHG activities enhances women's skills, leadership capacity, confidence, and participation in socio-economic development. The findings provide useful insights for policymakers, rural development agencies, and community-based organisations in strengthening SHGs as effective platforms for women's empowerment and sustainable rural development.

Key-Words / Index Term: Human Resource Management, Women Empowerment, Self-Help Groups, Rural Development, Economic Empowerment, Social Empowerment, Psychological Empowerment.

References

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Citation

Adevally Soujanya, "Gender Perspectives in Rural Employment: The Role of Human Resource Management in Empowering Women through Self-Help Groups in Telangana" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.3, pp.01-07, 2026. DOI: 10.5281/zenodo.23138127

Fault-Tolerant Software-Defined Networking (SDN) Design for Mitigating Control Plane Attacks

Mission Franklin

Research Paper | Journal Paper

Vol.6, Issue.3, pp.08-17, September-2026

DOI: 10.5281/zenodo.23140982

Abstract References Citation Download

Abstract

Software-Defined Networking (SDN) has transformed network management by separating the control plane from the data plane, enabling centralized control, programmability, and flexibility. However, this architectural paradigm introduces significant security concerns because the SDN controller becomes a critical point of failure and a primary target for cyberattacks. Control plane attacks such as Distributed Denial of Service (DDoS), controller hijacking, flow rule poisoning, and Byzantine failures can severely degrade network performance and availability. This study proposes a fault-tolerant SDN architecture designed to enhance control plane resilience against malicious attacks and operational failures. The proposed framework integrates distributed multi-controller deployment, Byzantine Fault Tolerance (BFT), intrusion detection mechanisms, secure communication protocols, and automated failover strategies. Reliability and availability models are developed to evaluate system performance under attack conditions. The architecture aims to improve network survivability, reduce downtime, and maintain consistent policy enforcement even in the presence of compromised controllers. The study contributes a comprehensive framework for building secure and resilient SDN infrastructures capable of supporting modern enterprise and critical infrastructure networks.

Key-Words / Index Term: Software-Defined Networking, Fault Tolerance, Control Plane Security, Distributed Controllers, Byzantine Fault Tolerance, DDoS Mitigation, Network Resilience.

References

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Citation

Mission Franklin, "Fault-Tolerant Software-Defined Networking (SDN) Design for Mitigating Control Plane Attacks" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.3, pp.08-17, 2026. DOI: 10.5281/zenodo.23140982

Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network

Mohd. Arshad Raza, Kiran Pandey

Research Paper | Journal Paper

Vol.6, Issue.3, pp.23-29, September-2026

DOI: 10.5281/zenodo.18193372

Abstract References Citation Download

Abstract

Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.

Key-Words / Index Term: Plant Disease Detection, Leaf, CNN, ResNet, Machine Learning, Confusion Matrix, Preprocessing.

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Citation

Mohd. Arshad Raza, Kiran Pandey, "Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network" International Journal of Scientific Research in Technology & Management, Vol.6, Issue.3, pp.23-29, 2026. DOI: 10.5281/zenodo.18193372