Recent Articles

Avani Shinde, Dr. Sonal Ayare  ·  International Journal of Technology and Emerging Research  ·  12 Nov 2025

This paper explores how Multimodal Artificial Intelligence (AI) combines diverse medical data—like images, text, physiological signals, and sensor data—to support real-time healthcare decisions. It highlights how integrating multiple data types enhances diagnostic accuracy, speeds up emergency care, improves surgical precision, and assists in chronic and mental health monitoring. The paper discusses fusion techniques (early, late, and intermediate) and key AI models such as CNNs, RNNs, and Transformers used for processing medical data. Major challenges include data integration, computational demands, privacy, and ethical regulation. Looking forward, it emphasizes the importance of explainable AI, personalized medicine, and the use of emerging technologies like 5G, edge computing, and IoMT (Internet of Medical Things). The conclusion asserts that multimodal AI will revolutionize healthcare by enabling precision medicine, proactive care, and better patient outcomes.

Milan Das , Shyamsundar Bairagya  ·  International Journal of Technology and Emerging Research  ·  08 Nov 2025

This study is structured to trace the genesis of temple architecture in India through a detailed examination of its historical, religious, and cultural underpinnings, and to explore how these foundational elements are reflected in the heritage temples that dot the Indian landscape today. It seeks to address the following key questions: What are the roots of temple architecture in India, and how did they evolve across time and regions? How did religious texts and philosophical traditions shape the conception and construction of temples? The methodology employed in this study combines historical analysis, textual interpretation, and comparative evaluation. Primary sources such as inscriptions, temple manuals, and archaeological reports are examined alongside secondary literature from historians, archaeologists, and architectural theorists. Case studies of significant heritage temples across different regions are included to illustrate the diversity and continuity of architectural traditions. The dissertation adopts an interdisciplinary approach, integrating perspectives from history, art history, religious studies, and heritage conservation to provide a holistic understanding of the subject. As India negotiates its identity in a globalized world, the recognition and preservation of its temple heritage become crucial for sustaining cultural continuity and fostering national pride. Understanding the genesis and reflection of temple architecture offers insights into the broader narrative of Indian civilization and contributes to the ongoing dialogue between tradition and modernity. In conclusion, the heritage temples of India are not static relics of the past but dynamic embodiments of a living tradition that continues to evolve. Their genesis is rooted in a complex interplay of spiritual vision, architectural innovation, and cultural expression.

Dr. Debastuti Dasgupta, Dr. Ishita Biswas  ·  International Journal of Technology and Emerging Research  ·  07 Nov 2025

The present study investigates the media framing of environmental and climate change issues, with a specific focus on the coverage of the 2025 California wildfires.Employing a content analysis methodology,it examines news articles from the digital editions of The New York Times(typically characterized as left-leaning)and Fox News(generally considered right-leaning).The analysis identifies recurring patterns related to tone, emotional language, attribution of causality, proposed solutions, allocation of blame, and representations of governmental response.The findings indicate that The New York Times predominantly frames the wildfires within the broader context of climate accountability and systemic governance failures.In contrast,Fox News frequently employs more sensationalist rhetoric,attributes the fires primarily to natural causes, and demonstrates a comparatively limited focus on long-term mitigation strategies.These results underscore the influential role of media framing in shaping public discourse on climate change and emphasize the importance of balanced, objective, and evidence-based environmental journalism.

Dr. Pratik Paun, Dr. Komal Patel  ·  International Journal of Technology and Emerging Research  ·  06 Nov 2025

The COVID-19 pandemic precipitated unprecedented economic challenges, particularly for Global South nations, characterised by disrupted trade, constrained fiscal space, and heightened debt vulnerabilities. This study conducts a comparative analysis of seven major regional trade blocs and economic cooperation groups SAARC, BRICS, G20, G7, Quad, EU, and SCO evaluating their contributions to economic recovery in the Global South from 2020 to 2024. Leveraging authentic statistical data from sources such as the IMF, World Bank, and UNCTAD, the research examines trade volumes, foreign direct investment (FDI), development finance, and GDP growth impacts. Findings indicate that BRICS and G20 have been pivotal in fostering recovery through innovative financial mechanisms and inclusive multilateralism, while SAARC’s impact remains limited due to geopolitical constraints. The study underscores the need for coordinated global economic strategies to ensure sustainable recovery in the Global South.

Anjum Ansari  ·  International Journal of Technology and Emerging Research  ·  06 Nov 2025

Social media usage and internet penetration have significantly expanded nationwide since the launch of the Digital India program, promoting social and economic development. But this digital growth has also led to a dramatic increase in cybercrimes, especially on social networking sites. In the context of Digital India, this research paper conducts an analytical analysis of the rise in cybercrimes, paying particular attention to social media abuse. It looks at the characteristics, origins, and trends of cybercrimes include financial fraud, identity theft, cyberstalking, online harassment, and the spread of false information. The paper assesses court interpretations and enforcement issues while critically analyzing the current legislative framework, particularly the Information Technology Act of 2000. It highlights the main weaknesses brought on by a lack of cybersecurity measures, a lack of digital literacy, and the ever-changing nature of cyberthreats. The study identifies weaknesses in the current regulatory and preventive systems using case studies and data analysis. In order to handle the new hazards, it also suggests extensive reforms that include technological, legal, and pedagogical measures. In order to protect the goals of the Digital India program, the results highlight the urgent need for a balanced strategy that fosters digital innovation while maintaining strong cybersecurity.

Mr. Sachin Manohar Patil, Mr. Atul Abhiman Khairnar  ·  International Journal of Technology and Emerging Research  ·  06 Nov 2025

Abstract: Academic libraries are changing a lot because of Artificial Intelligence (AI), thanks to artificial intelligence (AI) and they are becoming more flexible, efficient, efficient and welcoming places for learning. Inclusive learning environments. This paper talks about discusses how AI artificial intelligence is being used in academic libraries to make them better in terms of the improve their environment, their money, and their society. From smart clever ways of organizing organising books to user-adapted experiences tailored to users and predicting anticipating what people might may need, AI artificial intelligence is making library services more effective. Efficient. It also helps with contributes to the United Nations UN Sustainable Development Goals (SDGs) in general. The study looks at examples from around all over the world and finds out the main problems, like identifies key challenges such as ethical issues, unfairness in AI decisions, decision-making and lack of digital skills. Skills shortages. The paper document also gives sets out a plan roadmap for using AI in the fair and inclusive way. use of artificial intelligence. The findings show that when AI is used that, if applied in a fair and open way, it manner, artificial intelligence can really change how the way academic libraries are seen perceived and work operated in the 21st century.

Anuj Santosh Jagadale, Sakshi Bhoir, Nilesh Dnyaneshwar Koli, Vivek Parshuram Diavte, Pradnya K Ingle  ·  International Journal of Technology and Emerging Research  ·  04 Nov 2025

Turmeric (Curcuma longa) is a well-known Indian spice with powerful medicinal properties, mainly due to a compound called curcumin. Curcumin, along with its related compounds DMC and BDMC, is responsible for turmeric’s yellow color and its wide range of health benefits. This natural substance has been found to help with many health issues like inflammation, infections, diabetes, obesity, cancer, and even mental health problems such as anxiety and depression. Despite its potential, curcumin doesn’t dissolve well in water, which makes it harder for the body to absorb. However, when taken with piperine (from black pepper), its absorption improves significantly. In recent years, scientists have developed new ways, like using nanoparticles and combining curcumin with other medicines, to boost its effectiveness.

Dr. Kiran Jalem, Nandita Chowdhury, Debasish Samanta, Dinesh Mondal  ·  International Journal of Technology and Emerging Research  ·  04 Nov 2025

The morphometric analysis of a river basin provides critical insights into its hydrological and geomorphological characteristics, essential for effective watershed management and planning. This study presents a detailed morphometric analysis of the Gostani River Basin using Remote Sensing (RS) and Geographic Information System (GIS) techniques. High-resolution satellite imagery and topographic data, including Digital Elevation Models (DEMs), were utilized to extract drainage networks and basin boundaries. Key linear, areal, and relief morphometric parameters such as stream order, bifurcation ratio, drainage density, stream frequency, elongation ratio, and relief ratio were computed using GIS tools. The results reveal that the Gostani River Basin exhibits dendritic drainage patterns, moderate drainage density, and a sub-mature stage of geomorphic development, indicating semi-permeable sub-surface material and moderate to low relief. The analysis highlights the usefulness of RS and GIS in deriving accurate and comprehensive morphometric parameters, facilitating better understanding of basin dynamics for sustainable water resource management and environmental planning.

Urvish Gajjar  ·  International Journal of Technology and Emerging Research  ·  19 Oct 2025

The integration of large language models (LLMs) into mobile applications introduces adaptive tutoring, conversational question answering, and automated feedback generation, but it also breaks the deterministic input-output assumptions on which conventional mobile test automation relies. This paper proposes an AI-augmented testing framework for LLM-integrated e-learning mobile applications that combines automated test-case generation, a hybrid test oracle built from semantic similarity scoring and LLM-as-a-judge rubric evaluation, and cross-platform mobile UI execution using Appium. We present a layered system architecture that situates the AI test harness as a first-class component alongside the prompt orchestrator, retrieval-augmented knowledge base, and application microservices. We further describe an implementation using Python, pytest, sentence-transformer embeddings, and Appium WebDriver, and report an empirical evaluation comparing manual/scripted testing against the proposed approach across authoring effort, regression-cycle duration, defect-escape rate, and oracle false-negative rate. Results indicate substantial reductions in authoring time and regression duration alongside improved defect detection, while highlighting open challenges in oracle calibration and non-determinism of LLM outputs.

Jagisha  ·  International Journal of Technology and Emerging Research  ·  11 Oct 2025

Tomato is one of the most cultivated and consumed vegetable crops globally, but its yield is significantly threatened by a variety of diseases, primarily manifesting on the leaves. Early and accurate detection of these diseases is crucial for effective pest management and preventing substantial economic losses. Traditional methods, which rely on manual inspection by experts, are often slow, labor-intensive, and prone to human error. This survey paper provides a systematic and comprehensive review of the rapidly evolving field of automated tomato leaf disease detection, with a primary focus on deep learning (DL) techniques. We catalog a wide range of methodologies, from classical image processing and machine learning to state-of-the-art convolutional neural networks (CNNs) and vision transformers. The paper details publicly available datasets, discusses key technical challenges such as limited data, complex backgrounds, and real-time deployment, and analyzes the performance metrics of various approaches. Finally, we outline promising future research directions, including the integration of multimodal data, explainable AI (XAI), and the development of lightweight models for mobile and edge computing. This survey serves as a valuable resource for researchers and agricultural technologists aiming to understand the current landscape and contribute to advancing this critical application domain.

Prof. Bhavini Parmar, Prof. Sohilkumar Dabhi, Prof. Keyur Patel, Prof. Vasim Vohra, Kinjalben B. Dabhi  ·  International Journal of Technology and Emerging Research  ·  10 Oct 2025

Wireless Sensor Networks (WSNs) are vital for applications such as environmental monitoring and industrial automation, yet their limited energy resources and dynamic environments challenge network longevity and data reliability. Artificial Intelligence (AI) offers effective solutions through adaptive, energy-aware routing strategies. This paper investigates AI-based routing techniques—including deep reinforcement learning, fuzzy logic, swarm intelligence, and hybrid meta-heuristics—for dynamic path optimization in WSNs. These methods enable sensor nodes to make context-aware decisions based on factors like residual energy, link quality, node density, and traffic load. We review current state-of-the-art algorithms, conduct comparative performance analysis, and examine trade-offs in energy efficiency, latency, and computational cost. Simulation results demonstrate that AI-driven routing significantly enhances network lifetime and data throughput over traditional approaches. The findings highlight AI’s potential to drive intelligent, scalable, and energy-efficient routing for next-generation IoT-based WSNs.

Panchali Das, Dr Samit Chowdhury  ·  International Journal of Technology and Emerging Research  ·  09 Oct 2025

Human migration is the process of a person or group of people moving from one geographic location to another and changing their habitual place of residence permanently or semi-permanently. With its rich natural resources and fertile terrain, Assam continues to draw a sizable number of migrants from both inside and beyond the nation. The mechanism and magnitude of immigration, interstate migration, and internal migration have all made substantial contributions to the state's shifting demographic composition throughout time. According to the 2011 Census, Assam had 7,64,619 interstate migrants (2.43 percent of the total population) and 1,27,231 immigrants (from outside India) (0.40 percent of the total population). In comparison, 98,74,993 people migrated within the state of Assam in 2011, making up 31.44 percent of the state's total population. These numbers show a significant increase in movement volume over the 2001 Census, which is indicative of the state's shifting socioeconomic conditions and migration patterns. The primary source of secondary data used in this study is the 2001 and 2011 Census of India volumes. In order to show the spatial variations in migration trends throughout the state, the data has been analyzed using relevant statistical methods and depicted using appropriate cartographic approaches.

Geeta Rani, Sahul Goyal, Lalit Kumar Awasthi, Love Kumar  ·  International Journal of Technology and Emerging Research  ·  08 Oct 2025

Healthcare advances hinge on early and accurate disease detection, yet access to expert diagnostics remains uneven worldwide skin conditions, from benign rashes to malignant melanomas, affect millions and often go unrecognized until they progress to severe stages. Skin diseases manifest in diverse forms lesions, infections, and malignancies that demand precise differentiation to guide treatment and prevent complications. However, variability in lesion appearance, reliance on manual inspection, and limited specialist availability lead to misdiagnosis, delayed intervention, and increased healthcare burdens. Conventional methods such as dermoscopy and biopsy are time-consuming, subjective, and ill-suited to large-scale screening, underscoring the need for automated, scalable solutions. Deep learning excels at discerning complex patterns in medical images, offering rapid, objective analysis of skin lesions. To address these challenges, we propose a fine-tuned Xception model: leveraging ImageNet-pretrained depthwise separable convolutions, we unfreeze the final 30 layers for domain-specific feature refinement, integrate global average pooling and dropout to prevent overfitting, and employ the Adam optimizer with learning-rate scheduling and early stopping to ensure stable convergence. Trained on a balanced, augmented dataset of nine skin condition classes, our framework achieves 98.9 % overall accuracy, macro-average AUC of 0.997, and per-class F1-scores exceeding 0.98, while maintaining a compact 22 MB footprint for edge deployment. This approach not only delivers rapid, standardized diagnosis but also democratizes access to dermatological expertise, paving the way for broader adoption of AI in healthcare. It will help to grow a medical industry.

OMOREGIE Edoghogho, AITOKHUEHI Progress Ehimen, EHIGIATOR Goodness  ·  International Journal of Technology and Emerging Research  ·  03 Oct 2025

This study investigates the state of judicial independence in Nigeria during the Fourth Republic and its impact on the consolidation of democracy. The research addresses persistent challenges which includes Persistent encroachment by the executive branch which undermines judicial autonomy, particularly in appointments and removals of judges, Corruption and Lack of Accountability, Financial Dependence and Inconsistent Adherence to Constitutional Provisions. The study also explores the relationship between judicial autonomy and democratic stability, and proposes actionable reforms. The research employs a qualitative research design, utilizing content analysis of secondary data sources, including legal documents, scholarly articles, and reports. Landmark cases and reforms since 1999 are reviewed to assess the judiciary’s role in democratic consolidation. The research is anchored on the theory of Separation of Powers, emphasizing the necessity of distinct and independent branches of government for democratic sustainability. The framework posits that an autonomous judiciary is critical for checks and balances, protection of rights, and the legitimacy of democratic institutions. The study revealed the followings that, there is infinitesimal judicial independence in Nigeria, The judiciary’s effectiveness in arbitrating political disputes has contributed to periods of democratic stability, yet its compromised independence has also enabled electoral manipulation and undermined public confidence, Efforts such as the establishment of the National Judicial Council (NJC) and executive orders on financial autonomy have yielded some improvements, but enforcement remains inconsistent and vulnerable to political interests. The study provides some salient recommendation such as the followings, there is the need to Strengthen Financial Autonomy, vest the power of appointing and removing judges in an independent judicial commission, minimizing executive and legislative interference, Implement robust ethical standards, regular assessments, and disciplinary measures to combat corruption and restore public trust and Invest in training and capacity-building for judicial officers to uphold integrity, professionalism, and resilience against external pressures among others

Dr. Selvanayaki Kolandapalayam Shanmugam, Aniket G Patel  ·  International Journal of Technology and Emerging Research  ·  01 Oct 2025

As Large Language Models (LLMs) become more integrated into our daily lives, understanding their potential for social bias is a critical area of research. This paper presents a comparative analysis of bias in four small-scale and four large-scale LLMs, including several state-of-the-art models. In this study, these eight models were tested against a dataset of 200 questions designed to probe common social stereotypes across eleven categories, such as gender, race, and age. Then each of the 1,600 responses were classified as “Biased,” “Unbiased,” or a “Refusal” to answer. Our analysis reveals that the large models were significantly less biased (54.6% bias rate) than their smaller counterparts (67.8% bias rate), suggesting that increased model scale may contribute to a reduction in stereotypical outputs. In contrast, the small models were far more likely to refuse to answer sensitive questions (38.5% refusal rate vs. 8.9% for large models), indicating a fundamentally different approach to safety alignment. It was found that, while there was a slight negative correlation between a model’s refusal rate and bias rate, the relationship was not statistically significant, challenging the assumption that a reticent model is necessarily a fair one. Perhaps most importantly, it was observed that a huge range in performance even among the large models, with bias rates spanning from 20.1% to 85.9%. Since all the models tested are based on the same fundamental Transformer architecture, our findings suggest that social bias in LLMs is less a product of their architecture and more a reflection of the data, fine-tuning, and alignment strategies used to create them.

Shikha Goel, Pankaj Madan  ·  International Journal of Technology and Emerging Research  ·  29 Sep 2025

The emergence of the new generation of consumers (born 1997-2012) has transformed the principles of consumer behavior in the world, as the world has switched to the sphere of a digitally native and socially aware generation. Gen Z is projected to transform the concept of retail, marketing, and brand-consumer interaction in the next decade due to the estimated spending capacity of $12 trillion by 2030 (NASSCOM, n.d.). In contrast to earlier generations, Gen Z prioritizes authenticity, sustainability, transparency, and personalization, with a high emphasis on wellness and social responsibility. This research paper discusses the defining features of Gen Z consumers, the trends that determine the consumption pattern, and the implications for businesses in any industry. Based on the practitioner experience and scholarly opinion, the paper points out the need to adopt digital nimbleness, omnichannel approaches, and purposeful practices by businesses to create loyalty in this extremely volatile consumer population. The results highlight the importance of the fact that the brands that do not adjust to the demands of Gen Z are at risk of becoming irrelevant in a highly competitive landscape that prioritizes experience in the market.

Shiv Shankar Mishra, Pradnya Suhas Wathare  ·  International Journal of Technology and Emerging Research  ·  28 Sep 2025

The recent and rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) is affecting the sphere of auditing and financial reporting due to its potential to contribute to introducing a better accuracy level, effectiveness, and level of analysis. This study analyses the possibilities and threats of applying AI and ML in the auditing procedure and the financial reporting procedures. Applying natural language processing model and explainable artificial intelligence model, with the help of supervised and unsupervised machine learning methods, auditors can reveal anomalies, predict future wrong statements in a financial report and speed up decision-making activities. The research proposes the implementation of hybrid methodology of AI-led information and human professional judgment to achieve the integrity of the audit and ethical and regulatory consistency. Findings indicate that the employment of AI and ML can enhance efficiencies and automate repetitive operations, monitor conditions and keep checking against risks, but also enhance the quality of financial reporting by minimizing errors and maximizing the ease at which complicated datasets may be efficiently assessed. Nevertheless, the paper also highlights certain critical issues, e.g., the bias in algorithms, the drawbacks of the quality of data, cybersecurity threats, and even the severance of overreliance on automated products. The study points to the relevance of the governance framework, the ethical principles, and human supervision in maximizing the advantages of AI-based auditing and minimizing the risks related to the latter. The work has a contribution to the literature as it offers an integrated framework of embedding AI and ML in the audit and reporting activities that balance its performance in terms of efficiency, transparency and accountability.

Krishna Panda  ·  International Journal of Technology and Emerging Research  ·  21 Sep 2025

ABSTRACT Instead of being mutually exclusive, strong states and a strong centre are dependent on one another. Powerful centres could not exist without strong states, and vice versa. State-centre cooperation is facilitated by the Indian Federation. Disparities in race, religion, and culture undoubtedly point to a federal organisation, and intergovernmental cooperation is necessary. One of the most important factors in achieving the optimum results from cooperative federalism in India, a multi-party democracy, is political coherence. Even though the Indian constitution lays out the roles and powers of the union government and the state in detail, the government does not function in a vacuum. However, it is frequently observed that barriers arise when it comes to rendering decisions. This essay concentrates on how the objective of cooperating federalism is still unachievable due to these divergent political interests and beliefs. Additionally, this study offers suggestions for the future.

Pilla Sanjana, Dr. M. Ramjee  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

Bone fractures are a prevalent form of musculoskeletal injury that require timely and accurate diagnosis for effective treatment. Radiographic imaging, particularly X-ray analysis, remains the primary diagnostic tool. However, manual interpretation by radiologists is subject to human error, fatigue, and variability in judgment. This research presents a deep learning-based approach for the automated detection of bone fractures in X-ray images using Convolutional Neural Networks (CNNs). The proposed system is trained on a publicly available dataset comprising labeled images of fractured and non-fractured bones. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to improve model robustness and generalization. The CNN architecture was designed and optimized to learn distinguishing features from input images without manual feature engineering. The model achieved a high classification accuracy of over 93% on test data, demonstrating strong potential for assisting clinical diagnosis. Evaluation metrics including precision, recall, and F1-score indicate that the model can reliably differentiate between fractured and healthy bone structures. The system is scalable, cost-effective, and suitable for integration into computer-aided diagnostic tools, particularly in resource-limited settings. This study contributes toward the development of intelligent diagnostic systems that can support healthcare professionals by reducing diagnostic delays and enhancing patient outcomes.

Gullipalli Rohitha Sagar, P. Swathi, Prof. K. Venkata Rao  ·  International Journal of Technology and Emerging Research  ·  19 Sep 2025

Lung cancer is a leading cause of cancer-related mortality worldwide, and early detection is essential for improving patient outcomes. Traditional diagnostic methods rely heavily on radiologists interpreting chest CT scans, a process that is time-consuming and subject to inter-observer variability known as Medical Image Analysis. This study proposes a Convolutional Neural Network (CNN) framework for automated lung cancer diagnosis using CT images. The dataset was preprocessed through normalization and augmentation to enhance model robustness and generalization. The CNN model was optimized to classify images as cancerous or non-cancerous, with performance evaluated using accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrate high classification accuracy, indicating the model’s potential as a Computer-Aided Diagnosis (CAD) tool. Grad-CAM visualization further highlights discriminative regions, improving interpretability. This automated system offers a reliable, efficient approach to support radiologists, reduce diagnostic workload, and enhance clinical decision-making.