Recent Articles

Shivanand R Koppalkar  ·  International Journal of Technology & Emerging Research  ·  23 Apr 2026

This paper presents the design, architecture, and evaluation of a Retrieval-Augmented Generation system that assists new legal assistants in locating connected and similar past cases for new filings. The solution addresses Job 1, Legal Assistant, and leverages a curated Knowledge Base of 10 structured research session logs spanning five practice areas at a fictional law firm. Generative AI is assessed as capable of handling approximately 80 percent of the task, with the remaining 20 percent requiring human legal judgment, case validity verification, and jurisdiction-specific reasoning. The RAG architecture pairs a HuggingFace sentence transformer embedding model with FAISS vector search and GPT-4o-mini for grounded generation. Three query enhancement techniques improve retrieval precision beyond the baseline. Evaluation across eight metrics covering retrieval quality, generation quality through the RAG Triad, and operational performance demonstrates that the solution meets or exceeds the 0.80 target threshold on seven of eight dimensions. The paper documents limitations in cost, latency, case law currency, and the irreplaceable need for attorney oversight.

Dr, Sipra Karmakar  ·  International Journal of Technology & Emerging Research  ·  22 Apr 2026

This paper examines the application of Artificial Intelligence (AI) for conducting skill gap analysis in seafood processing and export units, with particular relevance to emerging seafood hubs where traditional workforce assessment methods remain prevalent. The seafood industry faces persistent challenges in maintaining product quality, regulatory compliance, operational efficiency, and sustainability, many of which stem from deficiencies in workforce skills. Conventional approaches are often slow, subjective, and incapable of delivering real-time insights. In contrast, AI enables data-driven, scalable, and predictive assessment of workforce competencies. The study proposes a multi-phase AI-driven framework that integrates machine learning, natural language processing, IoT-based monitoring, and predictive analytics to identify, measure, and address skill gaps across processing, logistics, compliance, and sustainability functions. Data sources include employee profiles, performance records, training histories, and industry benchmarks. AI tools such as computer vision for quality inspection, digital twins for process optimization, blockchain for traceability, and AR/VR platforms for training are analyzed for their role in enhancing workforce capability. Findings from a case study of a mid-sized seafood processing unit reveal significant deficiencies in advanced quality testing and export documentation knowledge. AI-based adaptive training improved compliance rates by 25%, reduced processing errors by 15%, and shortened export cycle time by 10%, demonstrating measurable operational gains. However, implementation challenges persist, including limited data availability, workforce resistance, digital literacy gaps, and high initial investment costs. The paper recommends the development of unified data platforms, change-management initiatives, scalable cloud-based AI solutions, and policy support through subsidies and skill-development programs. Overall, the study concludes that AI-enabled skill gap analysis can significantly enhance productivity, sustainability, and global competitiveness in seafood processing and export industries, providing a strategic pathway for modernization in an increasingly technology-driven global market.

Ranjan Majhi, Emdadul Islam, Pranshi Dixit  ·  International Journal of Technology & Emerging Research  ·  22 Apr 2026

Research misconduct is a serious incident in research. It is an unethical action in research. Here, researchers intentionally fabricate, falsify, and plagiarise data to support their research findings. Research misconduct reduces research integrity, standards, objectivity, transparency and credibility. The objectives of this review study are to discuss the concept of research misconduct, followed by discussing the reasons, consequences and prevention of research misconduct. This study also aims to study the current status of scientific or research misconduct. This study used various articles, journals, papers, and different guidelines such as COPE, WAME, ICMJE, etc. This investigation also examined different previous research works to discover the current status of research misconduct. The findings of this study explored the kinds of causes of research misconduct, such as lack of training, lack of awareness, pressure of publications, personal intention, etc. This investigation also found different outcomes, like it decreases academic reputation, it can damage carriers, it contributes to the loss of credibility of research work. Furthermore, it discovers that research misconduct is a common problem throughout the globe, including in India. This study shows the increasing rate of research misconduct from nineties (90s) to existing era. Most of the Indian or international studies mentioned plagiarism, fabrication, falsification, and personal intention as the reasons for misconduct in research work. This study will be helpful for the new academicians to gain a clear insight into research misconduct, its various causes, consequences, and prevention. This study also helps to do further research work after understanding the status of misconduct in research.

Aprajit Puri  ·  International Journal of Technology & Emerging Research  ·  22 Apr 2026

In the last ten years, the Kaladhungi area of Nainital district has undergone notable changes in land use influenced by socio-economic, environmental, and institutional elements. Historically focused on agriculture, the area is now experiencing a slow transition to non-agricultural pursuits, such as industrial growth and fruit tree farming. Diminishing interest from young people, shortages in labour, and heightened susceptibility to climate fluctuations have diminished the sustainability of conventional farming methods. As a result, numerous farmers are transforming farmland into orchards or selling it for commercial use, pursuing greater financial gains and less reliance on labour. Simultaneously, agricultural producers encounter various obstacles, including water shortages, conflicts with wildlife, soil deterioration, scattered land ownership, and restricted market opportunities, which all threaten the sustainability of farming. Policy measures, such as land reform rules and environmental safeguards, aim to manage land conversion and maintain ecological balance; nonetheless, gaps in implementation remain. Local governments, using a multi-level governance structure, are essential in overseeing land use changes, balancing progress with ecological preservation. The research underscores that the current transition from agriculture to other land uses signifies a wider rural change, raising worries about the sustainability of agriculture and livelihood security over time in the Himalayan area

Basen Hansda, Dr. Babita Das  ·  International Journal of Technology & Emerging Research  ·  21 Apr 2026

Abstract: This study inspects the impact of climate change on agriculture and food security among the Juang tribe in Kendujhar district of Odisha. The Juang Tribe is recognised as a Particularly Vulnerable Tribal Group (PVTG) and habitually depends on agriculture, forest resources, and cyclical labour for its livelihood. However, in recent years, climate change has meaningfully impacted their agricultural practices, crop productivity, and overall livelihood safety. Irregular rainfall patterns, increasing temperatures, and recurrent droughts have created serious challenges for traditional agriculture systems in the region. The main aims of this study are to understand the tribal community in the Panasanasa village, which is occupied by the Juang tribe, and to study their socio-economic and agricultural conditions in the context of changing climatic situations. The study also aims to find the various types of agriculture practised by the Juang people, including maintenance farming, shifting farming, and small-scale crop production. Another important objective is to analyse the economic disorder of tribal households and to recognize how climate change has influenced their income sources, agricultural productivity, and livelihood constancy. Furthermore, the study seeks to assess the level of food security among the Juang community and to explore how environmental changes affect their access to appropriate and nutritious food. This study is descriptive and systematically based on the examination of research questions. The study depends on field-based observations, interviews, and the collection of primary data from Panasanasa Juang villages in Kendujhar district. The results are expected to provide insights into the relationships among climate change, agriculture, and food security among tribal communities, and to highlight the necessity for sustainable agricultural practices, climate variation strategies, and helpful government policies.

Soni Rameshrao Ragho, Narendra Chaudhari  ·  International Journal of Technology & Emerging Research  ·  18 Apr 2026

The intensive development of new digital technologies, cloud computing, and networked systems made the amount and complexity of the digital evidence in cases of cybercrime investigation significantly greater. Manual and rule-based digital forensic techniques cannot manage large-scale heterogeneous and real-time data environments. Such systems are not always scalable, interpretable, and robust, which restricts their applicability in the current cyber threats. To address these issues, this paper suggests a Hybrid AI-Based Forensic Intelligence Framework that could be used to analyze digital evidence in scales and provide an explanation and real-time analysis. The suggested framework will combine some of the latest methods of artificial intelligence, such as machine learning, deep learning, and explainable artificial intelligence (XAI), to automate and improve the process of forensics. It helps in preprocessing data, feature extractions, anomaly detection, correlation of evidence and transparent decision making. The system can effectively handle a wide range of sources of data including system logs, network traffic, and multimedia artifacts using scalable hybrid models. Also, explainability properties provide legal reliability and transparency of forensic results. The experimental findings indicate that there are better accuracy, scalability, and reliability as opposed to traditional tools and single-model solutions. On the whole, the framework offers a powerful and intelligent approach to digital forensics in the modern context related to the investigation and making decisions more efficient in a complex cybercrime situation.

Kaushik Sinha, Debalina Sinha Jana  ·  International Journal of Technology & Emerging Research  ·  17 Apr 2026

We propose the Adversarially Robust Mask Generator (ARMG), a novel encoder network for deep learning-based steganography that simultaneously achieves high embedding fidelity and certifiable security against steganalytic attacks. Traditional steganographic methods often suffer from detectable artifacts or vulnerability to adversarial perturbations, hence limiting their practical deployment. The ARMG addresses these challenges by integrating a U-Net-style mask generator with adversarial training, gradient masking, and Lipschitz-bound certification into a unified framework. The mask generator produces pixel-wise perturbations constrained to preserve visual quality while embedding secret data, whereas a Vision Transformer-based discriminator adversarially trains the system to evade detection. Moreover, the inclusion of a certifiable robustness module ensures stability against input perturbations, providing formal security guarantees absent in prior GAN-based approaches. The proposed method employs residual dense blocks with channel attention for high-capacity embedding and introduces non-differentiable quantization to obfuscate gradients during white-box attacks. Experimental validation demonstrates that ARMG outperforms existing methods in both undetectability and robustness, achieving state-of-the-art performance across multiple steganalytic benchmarks. This work bridges the gap between adversarial robustness and steganographic security, offering a principled solution for real-world applications where both data hiding and resistance to analysis are critical.

Dr M Ponraj  ·  International Journal of Technology & Emerging Research  ·  17 Apr 2026

Brand loyalty has become a critical determinant of competitive advantage in India's rapidly expanding packaged agricultural goods market. This study examines the factors influencing consumer brand loyalty for packaged agricultural products in Karur City, Tamil Nadu. Using a structured questionnaire built around five core constructs — brand awareness, perceived quality, purchase intention, price sensitivity, and brand trust — data were collected from 170 consumers through purposive and stratified random sampling. Quantitative analysis was carried out using SPSS 26.0, employing chi-square tests, multiple regression analysis, one-way ANOVA, and structural equation modelling (SEM) through AMOS 24.0. The results reveal that brand trust (β = 0.341, p < .001) and brand awareness (β = 0.312, p < .001) are the strongest predictors of brand loyalty. ANOVA findings indicate statistically significant differences in brand loyalty scores across income groups (F(3,166) = 8.74, p < .001). SEM fit indices confirm an adequate model fit (CFI = 0.94, RMSEA = 0.057). The study underscores the importance of trust-building and consumer education strategies for agricultural marketers operating in Tier-II Indian cities.

Rohithkumarreddy Thatigutla, Dr. M. Humera Khanam, K Muni Vishnu, A Venkat Parthiv  ·  International Journal of Technology & Emerging Research  ·  16 Apr 2026

Alzheimer’s disease prediction requires robust mod eling of heterogeneous medical data, including structural MRI representations and structured clinical attributes. This study presents a controlled multi-modality benchmarking framework comparing optimized classical ensemble learning methods with a Variational Quantum Classifier (VQC) under identical prepro cessing and validation protocols. MRI features are reduced using Principal Component Analysis (PCA), while structured clinical attributes are modeled using Random Forest, XGBoost, Voting, and Stacking ensembles. A hybrid quantum–classical pipeline is implemented using Qiskit and PennyLane to evaluate near-term quantum feasibility under NISQ constraints. Experimental results demonstrate that stacking ensemble mod els achieve 95.3% accuracy on clinical data and 91.5% on MRI data, significantly outperforming the VQC, which achieves 71.4% accuracy under the same evaluation conditions. Statistical testing confirms that this performance gap is significant. These findings indicate that optimized classical ensemble learn ing remains superior for current medical prediction tasks, while variational quantum classification remains exploratory under present hardware limitations.

Priti Tukaram Chorade, Narendra Chaudhari  ·  International Journal of Technology & Emerging Research  ·  16 Apr 2026

Software-Defined Networking (SDN) has become a capable and programmable networking model which isolates the control plane and data plane in order to allow the management to be centrally located and network configurations to be dynamically configured. Although it has such benefits, the centralized character of SDN renders it very susceptible to Distributed Denial-of-Service (DDoS) attacks, which can significantly impair the network services and undermine the availability of the system. The traditional intrusion detection systems usually assume the signature-based approach or the supervised learning method that uses labeled attack data and cannot be effectively adjusted to dynamic network environments. To overcome these issues, the present study suggested a Drift-Aware One-Class Support Vector Machine (OCSVM) architecture in adaptive DDoS detection in Software-Defined Networks. The algorithm behind the suggested solution involves unsupervised anomaly detection to learn the challenge behavior of normal network traffic and detect deviations that are likely to signify an attack. Also, it includes a concept drift detection mechanism that is used to track this change in network traffic and implement the corresponding update to the detection model in case of significant shifts in the distribution. This ability to adapt to learning allows the system to retain accuracy of detection in the changing network conditions. Experimental analysis shows that the suggested drift-conscious OCSVM model outperforms the traditional anomaly detection methods on detection rates, minimizes false alarms, and strengthens it better. The findings underscore the usefulness of the unsupervised learning and drift-conscious adaptation in obtaining modern programmable network infrastructures.

Mayuresh Desai, Ms. R. S. Chavan, Adinath B Ghugare, Aniket L Methe  ·  International Journal of Technology & Emerging Research  ·  14 Apr 2026

This paper presents the design and development of an intelligent energy meter integrated with Internet of Things (IoT) technology for real-time monitoring and power theft detection. Traditional metering systems are unable to identify unauthorized consumption effectively, resulting in major revenue losses. The proposed system utilizes voltage and current sensors interfaced with a microcontroller to continuously monitor energy usage. The collected data is transmitted to a cloud platform for analysis. Any mismatch or abnormal pattern in consumption is treated as potential theft and triggers an alert. This system ensures transparency, improves efficiency, and reduces manual intervention. The solution is cost-effective, scalable, and suitable for modern smart grid applications.

Mr. Altaf Nalbandh, Dr. Neeraj Chavda, Dr. Rakesh Bumataria  ·  International Journal of Technology & Emerging Research  ·  12 Apr 2026

The growing emphasis on sustainable manufacturing has increased interest in ecofriendly lubrication strategies for machining. Conventional flood cooling, despite its effectiveness, is associated with high fluid consumption, waste disposal concerns, and occupational health risks, motivating the use of near-dry alternatives such as minimum quantity lubrication (MQL). This study investigates the effect of mono and hybrid nanofluids under MQL on the turning performance of AISI 1040 steel. Three input parameters, cutting speed, feed rate, and nanoparticle weight percentage, were evaluated against three responses, cutting force, cutting temperature, and surface roughness. The experiments were designed using Response Surface Methodology with a Box–Behnken design to develop second order regression models and perform multi-response optimization. The models showed good agreement with the experimental results, confirming their predictive reliability. Among the tested conditions, the hybrid nanofluid Case E (75:25 Al2O3:ZnO) provided the best overall performance. The optimal combination of 31.71 m/min cutting speed, 0.11 mm/rev feed, and 1 wt% nanoparticle concentration produced the highest desirability. The findings indicate that mono and hybrid nanofluids under MQL can improve machining performance, with Case E offering the best balance of thermal control and friction reduction. However, the conclusions are limited to the studied parameter range and setup.

Vivek Kumar Saxena, Astha Mishra  ·  International Journal of Technology & Emerging Research  ·  09 Apr 2026

The integration of Artificial Intelligence (AI) tools into academic research is increasingly reshaping the processes of knowledge creation and dissemination. This study investigates the adoption, applications, and impacts of emerging AI tools among researchers across various disciplines. Utilizing a mixed-methods approach, the research combines quantitative survey data from 150+ academics with qualitative insights drawn from semi-structured interviews. The results indicate widespread use of AI-driven platforms such as ChatGPT, AI Essay Writer, Research Gap Finder, AI Paraphraser, Elicit, and AI-enhanced reference management tools – Mendeley, Zotero etc…, primarily supporting literature review, data analysis, and academic writing. Participants report enhancements in research efficiency and quality but also express concerns about ethical challenges including data privacy, academic integrity, and bias. Significant disciplinary differences in AI adoption and perceptions are identified. The study underscores the urgent need for comprehensive institutional guidelines to promote responsible AI integration in research workflows. These findings contribute to the discourse on digital transformation in academia and provide actionable recommendations for researchers and policy-makers to harness AI’s potential while addressing its risks.

Anuj Santosh Jagadale, Vivek Parshuram Diavte, Nilesh Dnyaneshwar Koli, Neha Agarwal  ·  International Journal of Technology & Emerging Research  ·  07 Apr 2026

Fuel distribution is the final engineering frontier of the hydrocarbon lifecycle, where refined molecules transition into economic momentum. This paper presents a structured review of fuel logistics execution—from refinery dispatch systems to retail penetration—while highlighting the strategic evolution of market-adaptive fuel distribution. It evaluates multimodal transport networks, storage terminal innovations, demand-responsive fuel formulation, regional pricing adaptation, regulatory synchronization, and commercialization strategies that shape modern fuel accessibility. The study emphasizes that downstream logistics is no longer a passive supply operation but an intelligent, market-sensitive distribution ecosystem balancing quality preservation, cost efficiency, safety, and real-time demand dynamics. The paper concludes that the future of fuel logistics lies in adaptive supply networks supported by automation, real-time analytics, and regulatory-aligned market responsiveness.

S. Radha Krishna Reddy, Dr.J.B.V. Subrahmanyam, Dr. A. Srinivasula Reddy  ·  International Journal of Technology & Emerging Research  ·  07 Apr 2026

The study introduces a new Adaptive Sliding-Mode Control approach that involves the application of artificial neural networks in the control of Static Synchronous Compensator systems coupled with Self-Excited Induction Generators of Wind Energy Conversion Systems. The proposed ASMC is guided towards enhancing the voltage regulation, reactive power support and stability at varying wind and fault conditions. The ASMC, in contrast to traditional Sliding-Mode Controllers (SMC) is an adaptive control system applying control gains dependent on system conditions reducing disturbances and improving resilience to parameter uncertainties and disturbances. The overall dq-axis plant model of SEIGSTATCOM system is constructed and simulated in MATLAB/Simulink to test the transient response, power quality and low-voltage ride-through (LVRT) capability. The results of simulation indicate that the ANN-enhanced ASMC has superior voltage stability and faster recovery in 40% voltage sags as a result of three-phase faults, and decreases Total Harmonic Distortion (THD) and reactive oscillations of power. The ANN-ASMC provides a smoother control action, better fault-ride-through, and smoother reactive power compensation in comparison with the SMC and PI controllers, which validates its use in the modern wind power systems that require high reliability and power quality.

Dr, Sipra Karmakar, Dr. Susant Kumar Mishra, Dr. Shradhanjali Panda, Dr. Saroj Kumar Sahoo  ·  International Journal of Technology & Emerging Research  ·  31 Mar 2026

Given that Odisha is a coastal state, the seafood industry is one of its most promising industries. With 8.16 lakh MT of fish produced in 2019–20, Odisha is the fourth-largest fish-producing state in India, according to the Information & Public Relations report from 2021. It made about 6% of all the fish that was produced in India. A large number of business establishments participate in this broad industry by processing seafood, exporting it to overseas markets and generating foreign exchange for the state. At this critical juncture, the sector cannot afford to ignore practices that prioritize environmental, economic, and social sustainability in order to enhance capacity building and make efficient use of land. Thus, the importance of sustainability is realized. Sustainable seafood is produced by fisheries and aquaculture businesses that reduce their negative effects on the environment, provide safe and equitable working conditions, and promote economic gains and livelihoods along the whole supply chain. The current study intends to investigate the growth trends of the seafood sector in the state in order to comprehend the business outcome and various challenges faced by companies operating in Odisha. For that reason, ten business establishments in Odisha are taken into account. The study's goal is to investigate how the state's seafood industry is changing through a strategic approach that includes SWOT analysis, growth trend analysis, and actions taken to achieve sustainability. Primary data, which was gathered via questionnaires and interviews, as well as secondary data, were utilized and interpreted in the present research work.

Shivanand R Koppalkar  ·  International Journal of Technology & Emerging Research  ·  30 Mar 2026

The report conducts an analysis of Amazon’s digital transformation through the lens of emerging technologies, focusing on artificial intelligence (AI)-driven inventory forecasting, robotic automation, computer-vision checkout systems, and autonomous delivery robots. This report integrates the framework of dynamic capabilities and socio-technical systems theory as well as resource-centered perspective and to investigate the strategic logic underpinning Amazon’s technological investments and their operational consequences. Through this tri-theoretical lens, it demonstrates how Amazon leverages valuable, rare, and inimitable resources while continuously reconfiguring its capabilities to adapt to evolving technological and organizational environments. Furthermore, by employing theory of sociotechnical systems, the analysis explores the interplay between Amazon’s technological infrastructure and its human and organizational elements, illuminating how this dynamic interrelation shapes performance outcomes and fosters sustained competitive advantage. It evaluates measurable outcomes such as cost savings, productivity gains, and customer experience enhancements while considering associated risks and ethical concerns. The results suggest that advances in AI, robotic automation, and the Internet of Things operate as high-value, hard-to-replicate assets that deepen Amazon’s competitive position., yet their deployment raises challenges related to privacy, workforce impacts, and infrastructure complexity. The report concludes with recommendations for leveraging emerging technologies within a balanced socio-technical framework.

R Bala Rangaiah , Dr. Sahil  ·  International Journal of Technology & Emerging Research  ·  13 Mar 2026

The contemporary jurisprudence and scenario of the corporate governance in India as well as globally has gone through a drastic systematic change which has not only shaped the corporate structure concerning board of directors, shareholders, subscribers, members but has also altered the concerns revolving around the inclusion of the vulnerable communities in the decision making procedure of the corporate governance. The situation of moving toward a comprehensive stakeholder-centric approach and away from the conventional theory of shareholder primacy is another significant change brought to corporate governance. Diversity, Equity, and Inclusion (DEI) are now seen as crucial markers of organizational resilience, human capital efficiency, and Environmental, societal, and Governance (ESG) performance rather than just optional societal obligations in this developed paradigm. “The broader umbrella of Diversity and inclusivity has been a strong witness of the establishment of the level playing field where people from every community can strive towards excellence without having to face the obstruction of discrimination and hostility meted out by the society. For India, a nation currently navigating a transition from historical marginalization to legal recognition of transgender identities, these global lessons provide a vital roadmap. The integration of the transgender community into the formal economy is not just a moral imperative but a strategic necessity, particularly as regulatory bodies like the Securities and Exchange Board of India (SEBI) mandate increasingly granular reporting on social inclusion.”

Shivanand R Koppalkar  ·  International Journal of Technology & Emerging Research  ·  20 Feb 2026

This strategic framework delineates an integrated approach to establishing artificial intelligence capabilities within Innovate Software Consulting Inc Ltd, a consulting organization specializing in Oracle Human Capital Management Cloud solutions, enterprise credit risk assessment platforms, and healthcare information technology integration. The organization's two-decade operational history provides foundational expertise upon which advanced AI competencies can be systematically constructed. Contemporary scholarship documents fundamental organizational restructuring catalyzed by artificial intelligence adoption, characterized by migration from traditional hierarchical governance toward distributed decision architectures (Fountaine, et. al., 2019). The proposed framework responds to critical capability gaps organizations encounter when attempting to operationalize AI technologies: insufficient technical expertise, unclear accountability structures, inadequate governance mechanisms, and misalignment between technological investments and strategic business objectives (Ransbotham, et. al., 2020). The architectural foundation employs a centralized-decentralized hybrid model incorporating four interdependent organizational strata. Strategic oversight resides with Executive Leadership establishing organizational vision, resource allocation priorities, and performance expectations. Technical coordination functions through an AI Center of Excellence providing specialized expertise, methodological standardization, and knowledge transfer mechanisms across the enterprise (Bersin, 2019). Operational execution occurs within Cross-Functional Project Teams combining domain expertise, technical capabilities, and client relationship management competencies. Ethical oversight and regulatory compliance operate through dedicated Governance Committees ensuring responsible AI deployment aligned with established frameworks and organizational values. Recent organizational behavior research emphasizes leadership adaptation requirements accompanying AI system integration, particularly concerning decision authority redistribution, workflow reconfiguration, and performance feedback mechanisms (Lebovitz, et. al., 2021). Leaders must develop capacities for human-AI collaboration orchestration, algorithmic transparency communication, and bias mitigation across sociotechnical systems (Wilson & Daugherty, 2018). The framework specifies comprehensive implementation components addressing role specifications with requisite competency profiles, collaborative protocols governing internal team coordination and external stakeholder engagement, strategic alignment methodologies connecting AI initiatives to organizational objectives, risk management strategies addressing technical, ethical, and operational challenges, and temporal deployment sequencing across quarterly implementation phases throughout calendar year 2026. Performance assessment encompasses multidimensional evaluation criteria: technical proficiency measures examining model accuracy and system reliability; fairness metrics detecting demographic biases and differential impacts; transparency standards ensuring explainability and stakeholder comprehension; accountability mechanisms establishing decision traceability; business value quantification through operational efficiency gains and revenue impact; and team health indicators monitoring employee satisfaction, retention, and capability development (Brynjolfsson & McAfee, 2017). This structured approach positions the organization to capitalize on AI-driven transformation opportunities while preserving the consultative integrity and client confidence characterizing its established market position.

Sayan Bose, Milan Das , Shyamsundar Bairagya  ·  International Journal of Technology & Emerging Research  ·  07 Feb 2026

Massive open online courses (MOOCs) have received both praise and condemnation in higher education. Massive open online courses (MOOCs) debuted in 2008, and the phenomenon quickly acquired global traction. The present study aims to increase understanding of the effects of MOOCs on higher education and teachers' attitudes towards this emerging type of education by analysing existing research and data. The current study is quantitative, and the survey method of data collection was chosen due to the nature of the study and its demand. The population of the study included all higher education teachers of West Bengal. The analysis shows that there is no significant difference in male and female teachers' attitudes towards Massive Open Online Courses (MOOCs), nor is there a significant difference in teachers' attitudes towards Massive Open Online Courses (MOOCs) based on teaching experience. The major findings show that MOOCs have a positive influence on the attitude of higher education teachers.