VOLUME 9 Issue-1 VOLUME - 9 ISSUE - 1 JANUARY- 2026

5. AN INTELLIGENT DEEP LEARNING FRAMEWORK FOR BRAIN TUMOR DETECTION

Author: Mrs.S.S.Shanthi, S.Praveen

Accurate detection of brain tumors from magnetic resonance imaging (MRI) plays a vital role in clinical diagnosis and treatment planning. This paper proposes an intelligent deep learning–based framework for automated brain tumor detection using transfer learning with the InceptionV3 convolutional neural network. The proposed framework categorizes brain MRI images into four clinically relevant classes, namely glioma tumor, meningioma tumor, pituitary tumor, and no tumor. Pretrained ImageNet weights are employed to leverage rich feature representations, while the feature extraction layers of InceptionV3 are frozen to minimize overfitting and reduce computational complexity. Custom fully connected layers are integrated to enable effective multi-class classification. Furthermore, a prediction module is developed to classify unseen MRI images and provide confidence scores, facilitating real-world clinical applicability.

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4. A Study on India’s Export Competitiveness in the Malaysian Market: Challenges and New Opportunities

Author: Dr. T. Kanimozhi, Dr. B. Poornima, Dr. V. Mythili

The study examines India’s export competitiveness in the Malaysian market with a focus on key challenges and emerging opportunities. Indian exporters face issues such as intense competition, non-tariff barriers, high logistics costs, and stringent quality and sustainability standards. At the same time, opportunities arising from supply chain diversification, digital trade, e-commerce, and demand for value-added and green products offer scope for growth. Based on secondary data, the study concludes that policy support, improved infrastructure, MSME development, and effective use of trade agreements are crucial to enhancing India’s export performance in Malaysia.

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3. Real-Time IoT Anomaly Detection Using the Threshold-Guided Lightweight Voltage Network (TGL-VN)

Author: Dr.R.Harinisree

The rapid growth of Internet of Things (IoT) networks has significantly increased security vulnerabilities, demanding real-time, accurate, and lightweight anomaly detection mechanisms suitable for resource-constrained edge devices.

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2. A STUDY ON ADOPTION OF GREEN HUMAN RESOURCE MANAGEMENT PRACTICES IN THE INFORMATION TECHNOLOGY SECTOR

Author: Dr.B.Karthikeyan, Dr.R.Sowmiya

The study aimed to examine the adoption of Green Human Resource Management (GHRM) practices in the Information Technology sector and to identify the factors influencing their implementation. Primary data were collected from 150 respondents using structured questionnaires. Descriptive statistics and one-way ANOVA were applied to analyse the data. The results revealed that green recruitment was the most widely adopted practice, along with energy-efficient workplace policies and sustainability training. ANOVA results indicated significant differences in adoption based on organizational size and type. The study concluded that mid-sized and large IT companies demonstrated greater commitment to sustainability through GHRM initiatives, emphasizing the need for eco-conscious HR strategies in the sector.

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1. SMART CYBER BREACH MONITORING AND DETECTION USING MACHINE LEARNING MODELS

Author: Mrs.S.S.Shanthi, S.Kaviyaprabha

In an era of increasingly sophisticated cybersecurity threats, the development of robust prediction and detection systems to protect against cyber hacking breaches has become essential.

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