Volume 13 | Issue 4 |

 Abstract

Terrorism continues to be a significant threat to global peace and constitutional democracies, requiring states to enact robust counter-terrorism laws. However, these laws often provoke a complex debate between ensuring national security and protecting civil liberties. This research review critically examines the legal tension between state imperatives and individual rights through a comparative lens, focusing on "India, the United States, the United Kingdom, and international frameworks like the United Nations Security Council Resolutions. It highlights the absence of a universally accepted legal definition of terrorism, creating ambiguity and potential misuse". The evolution of India's anti-terrorism laws--from TADA to UAPA--demonstrates the challenges of balancing security with human rights. Comparative experiences from the U.S., U.K., and the European Union showcase varying degrees of success in maintaining this balance, with frameworks like the European Convention on Human Rights offering vital safeguards. Key civil liberties impacted include freedom of speech, privacy, and access to legal counsel. Judicial review mechanisms, particularly in India, reveal both progress and limitations in curbing executive overreach. Landmark case studies further illustrate the role of courts in defending constitutional values. The review concludes with policy recommendations: the necessity for precise legal definitions, enhanced judicial review, sunset clauses, independent oversight mechanisms, and human rights-compliant procedures. Ultimately, the research underscores the importance of a rights-centric approach to counter-terrorism that aligns security measures with democratic principles and constitutional guarantees, ensuring that the pursuit of national security does not erode the very freedoms it seeks to protect.

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 Keywords

Terrorism, India, United States, United Kingdom, United Nations, Security Council Resolutions, TADA, UAPA, Human Rights and Legal Counsel

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

NISHULK EVAM ANIVAARYA SHIKSHA EVAM VARTAMAAN MEIN ISKI UPALABDHATA KE PRATI ABHIBHAAVAKON KI ABHIVRTTI

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NISHULK EVAM ANIVAARYA SHIKSHA EVAM VARTAMAAN MEIN ISKI UPALABDHATA KE PRATI ABHIBHAAVAKON KI ABHIVRTTI

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The advancement of women is the key strategy for their empowerment and holistic well-being. Empowering women facilitates their acquisition of rights, personal development, decision-making capabilities, and self-confidence. Women in tribal societies are deprived of the authority to own power and assets necessary for autonomous decision-making. Owing to their fragility, individuals frequently encounter challenges in their daily lives and lack a steady livelihood or consistent revenue source. Economically empowered women can foster effective leadership and exhibit enhanced decision-making capabilities. Self-Help Groups were established for impoverished rural women to combat poverty by social mobilisation and active engagement in the development process. The self-help group facilitates financial support and sustained development to eradicate poverty and foster effective socio-economic empowerment for beneficiaries. This report examines the influence of Self-Help Groups (SHGs) on tribal women in the Annamayya district of Andhra Pradesh.

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Self-Help Groups, Tribal women,Empowerment, Standard of living

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Creative Commons Attribution 4.0 and The Open Definition

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Computer networking forms the foundation of modern communication systems, enabling information sharing and connectivity across the globe. From humble beginnings with ARPANET to the vast expanse of today's internet and the emergence of next-generation networking paradigms like 5G and edge computing, the field has witnessed significant evolution. This survey provides a comprehensive examination of the history, architectures, protocols, topologies, and emerging trends in computer networking. We emphasize both traditional structures and disruptive technologies, aiming to deliver a holistic understanding of this dynamic field.

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Computer Networks, TCP/IP, OSI Model, Software-Defined Networking, 5G, IoT, Cybersecurity, Edge Computing

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 Abstract

Anukuli Siksha Taknik Aur vyanktigat Nirdesh

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Anukuli Siksha Taknik Aur vyanktigat Nirdesh

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Tumor detection in the brain is considered a crucial effort in imaging techniques where the diagnosis made precisely and early can change the scenario in healthcare. Driving into their applications, deep learning effectiveness comes from systems where convolutional networks, under the generals of recurrent models, are well-articulated, such as long short-term memory. Thereby in the study, we came up with a hybrid model for deep learning that integrates CNN-LSTM as a base model and VGG 16 feature extractor to improve on the performance of brain tumor classification; the CNN-LSTM model is spatial and temporal dependencies extraction from MRI images while deep feature representation is facilitated by VGG16. The ensemble model improves classification accuracy by jointly preparing spatial hierarchies and sequential dependencies for data. Testing and training was accomplished from a brain tumor dataset on a very high accuracy and robust performance. Experimental results therefore show that the hybrid VGG-LSTM model is superior in brain tumor detection over conventional CNN. Thus, clinicians dealing with diagnosis could be assisted with a very easy tool by radiologists.

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Keywords-- Brain Tumor detection, deep learning, image processing, CNN.

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Skin cancer remains one of the most commonly diagnosed cancers worldwide, with its incidence rising steadily due to increased exposure to ultraviolet (UV) radiation and other environmental factors. Early and accurate detection is critical in improving patient survival rates and reducing the burden of invasive treatments. This paper reviews state-of-the-art approaches to skin cancer detection, with a focus on leveraging ML algorithms and advanced image processing techniques. Specifically, it discusses the application of deep learning models, particularly convolutional neural networks (CNNs), in analyzing dermoscopic images for identifying malignant and benign skin lesions such as melanoma, BCC, SCC and other common skin lesions. These methods demonstrate significant potential in achieving high diagnostic accuracy by analyzing features like color, texture, and lesion asymmetry. Furthermore, the paper highlights the challenges faced in developing robust systems, including data diversity, algorithm transparency, and clinical integration. By synthesizing findings from recent advancements, this work underscores the transformative role of AI in dermatology, paving the way for accessible and efficient diagnostic tools to combat the global skin cancer burden.

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Skin lesions, skin cancer, machine learning, deep learning, CNN, KNN, SVM, early detection, dermatology.

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Creative Commons Attribution 4.0 and The Open Definition

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Voltage sags are among the most frequent power quality disturbances in electrical distribution systems, often caused by short-circuit faults, equipment switching, or load variations. These sags can lead to substantial operational disruptions, particularly in industries with sensitive electronic equipment, and result in economic losses due to downtime, equipment malfunction, and data corruption. Mitigating voltage sags is thus essential to ensure the reliability and stability of power systems, especially in critical sectors such as manufacturing, healthcare, and data centers.This paper presents an in-depth analysis of the Dynamic Voltage Restorer (DVR) as an effective solution for voltage sag mitigation and overall power quality enhancement. The DVR is a power electronic device that operates by injecting a compensating voltage into the system during a sag event, thereby restoring the voltage to its nominal level within milliseconds. The system's architecture, which includes an energy storage device (such as a capacitor or battery) and a voltage-source converter, ensures fast response times and minimal interruption to sensitive loads.

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Creative Commons Attribution 4.0 and The Open Definition

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Dynamic Voltage Restorer (DVR), voltage sag mitigation, power quality improvement, control strategies, smart grids, energy storage, voltage-source converter, predictive control, industrial power systems.

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Creative Commons Attribution 4.0 and The Open Definition

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This project focuses on the design and implementation of an advanced control strategy for electric vehicles (EVs) using a Permanent Magnet Synchronous Motor (PMSM) drive system. PMSMs are widely preferred in modern EVs due to their high efficiency, power density, and precise torque control capabilities. The proposed control architecture integrates Field-Oriented Control (FOC) for dynamic performance enhancement, ensuring efficient speed and torque response under varying load conditions. The system also incorporates regenerative braking and battery management considerations to optimize energy usage. MATLAB/Simulink simulations validate the effectiveness of the controller, demonstrating fast transient response, high efficiency, and robustness to parameter variations. This work contributes to the development of reliable and energy-efficient EV propulsion systems, supporting the transition toward sustainable transportation

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ELECTRIC VEHICLE CONTROL USING PMSM DRIVE,PULIVENDULAKADAPADISTRICT, ANDHRA PRADESH

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ABSTRACT Purpose - This research paper is intended to analyze employee's perceptions about social media usage in job search and to identify the differences in their perceptions based on demographic and social media usage related variables. Methodology- For the purpose of this study, a sample of 514 employees of IT firms from South Region was collected to know their opinions about social media usage in job search. A self administered questionnaire including Five-point Likert type numerical scale ranging from Strongly Disagree to Strongly Agree was used. In order to know the differences in employees' perceptions, independent samples t- test and One Way ANOVA was applied. Findings - The research outcomes indicated that a majority of the employees were agreed about social media being an effective tool for job search. Thus the results of the present study support that social media is an effective tool for job search from employees' perspective. However, a significant difference was found in employees' perceptions about the effectiveness of social media based on age, level of employment, no. of years of social media usage, and time spent, while gender is found to have no impact on their perceptions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

social media in recruitment, effectiveness of social media, effective job search

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Creative Commons Attribution 4.0 and The Open Definition