andnbsp;The outbreak of seasonal crop diseases is a major risk to world food security and agricultural economies and the problem brings about massive loss in billions of dollars every year. Most disease management methods have been based on responding to the disease hence reducing their effectiveness in the control of the prevalence of the disease. In this review, the emerging paradigm of artificial intelligence-based predictive models in forecasting seasonal crop diseases is critically reviewed, especially on the architecture of deep learning models for temporal forecasts, and their economic consequences. Recent time-series modelling

techniques such as Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCNs) are synthesized based on meteorological, phenological, and pathological data and used to predict the occurrence of a disease with lead times long enough to implement preventive measures. Additionally, we discussion economic modelling frameworks, which quantify the monetary advantages of early predictionandnbsp; systems by increasing yield maintenance, diminished applications of pesticides and optimized resource directing by mathematical calculation. Case studies across large systems of crop-pathogens, such as rice blast, tomato late blight and wheat rust reveal that the prediction accuracy was higher than 85% and possible economic returns on investment were in the range of 300-500. We find such major flaws in extant literature such as inadequate incorporation of socioeconomic factors, lack of participation in various agroecological regions, and lack of strategies to deploy them to farmers. This review offers an all-inclusive basis to the researchers, policy-makers, and all interested agricultural stakeholders who might want to use AI technologies to promote sustainable disease management and economic health in agriculture.

This paper presents football analytics, focusing on match event data and its use in performance analysis. Match event data consists of precise, time-stamped recordings of on-ball events such as passes, shoots, and tackles, which capture essential aspects of games. The study reviews data sources, analysis methodologies and key performance metrics. Practical applications, including player evaluation and tactical assessment, demonstrate how event data inform strategic decisions. The results show how important it is to include event data in performance analysis. The findings also highlight the problems, limits, and future directions of football analytics.

This study explores ethical AI in FMCG advertising, showing that bias-free personalization delivers both profitability and inclusivity. Campaigns applying fairness and transparency achieved 12andndash;15% higher conversions and an 18% rise in consumer trust. Regression analysis confirmed positive effects on trust (andbeta; = 0.27, p andlt; 0.01), loyalty (andbeta; = 0.32, p andlt; 0.01), and purchase intention (andbeta; = 0.21, p andlt; 0.05). Bias detection further showed Demographic Parity Difference reduced from 0.18 to 0.06. Contrary to the assumption that safeguards increase costs, ethical personalization expanded underserved market reach by 12% and mitigated reputational and regulatory risks. The results highlight that fairness-driven AI fosters consumer satisfaction, sustainable innovation, and inclusive growth in the FMCG sector.

Efficient task scheduling is the most important task in cloud computing system for the optimal performance

optimization. Traditional informed searches like the First-Come-First-Served (FCFS) and Round Robin often fail to account for the dynamic nature of the modern-day tasks and workloads, which increases the latency and fails to utilize resource properly. Our paper proposes a Machine learning (ML) driven approach for the correct and efficient prediction of task waiting time and schedule the tasks intelligently. We have used a public cloud workload dataset which includes 5000 job entries, and have implemented a Random Forest regression model to analyze the impact of multi-dimensional features like CPU utilization, memory consumption and network bandwidth. The results demonstrate that Machine Learning models can effectively identify the performance matrices and the network bandwidth has emerged as the primary predictor of task latency. Our proposed framework provides a foundation for smart schedulers which can dynamically allocate the resources which reduces the error rates significantly and also enhances the system throughput.

Automatic Speech Recognition (ASR) converts the spoken words/sentence to text, which allows voice activation features, accessibility, and real-time translation to all. In low resource languages, ASR opens the

door to digital inclusion, heritage conservation, and community-based data collection in areas where there were previously limited resources. We consider an ASR system of the Sylheti language that was created on

the Whisper model of Open AI. Sylheti is a language with low resources, used in India and Bangladesh, but there are no significant digital and linguistic resources, which makes it challenging to develop the ASR. A hybrid dataset was developed to fill this gap by the addition of a custom Sylheti corpus and Common- Voice dataset Bengali corpus. The audio information was pre-processed by removing noise, cutting, and matching the audio with written transcriptions. The small model of the Whisper was also optimized with the help of the combined dataset, as its model is based on the transformer-based encoder decoder architecture, which was trained on the task of multilingual transcription. Word Error Rate (WER) metrics is used to evaluate trained model. The system got a WER of 66.8% which proved to be a good system in transcription accuracy of Sylheti speech. The system was implemented via an interactive Gradio interface of interactive transcription. Its results confirm Whisper to be flexible towards low-resource languages and the possibility of facilitating linguistic inclusivity, cultural continuity, and accessibility.

Machine unlearning has emerged as a critical re- search direction in response to growing concerns about data

privacy, regulatory compliance, and the ethical deployment of artificial intelligence systems. With regulations such as the General Data Protection Regulation (GDPR) enforcing theandldquo;right to be forgotten,andrdquo;traditional machine learning paradigmsandmdash;where models permanently retain learned informationandmdash;are increasingly inadequate. This study presents a comparative analysis of emerging model technologies and existing survey studies on machine unlearning, examining how contemporary architectures address data removal, privacy guarantees, and computational efficiency. The analysis categorizes unlearning techniques into exact,approximate, federated, and verification-based approaches, and evaluates their applicability across traditional machine learning models, deep neural networks, transformer architectures, and generative models. The paper further reviews major survey contributions to identify common taxonomies, evaluation metrics, and open research challenges. Special emphasis is placed on emerging generative systems and large-scale foundation models, where latent memorization and parameter complexity complicate effective unlearning. Comparative findings highlight trade-offs between computational cost, scalability, and privacy robustness, revealing that while exact unlearning ensures strong theoretical guarantees, approximate and optimization-based methods offer practical scalability for modern deep models. Additionally, the study identifies gaps in standardized verification protocols and benchmarking practices across surveys. By synthesizing current advancements and limitations, this work provides a structured foundation for future research aimed at developing efficient, verifiable, and scalable machine unlearning mechanisms for next- generation AI systems.

The paradigm of QML is a transformative and cutting- edge research domain that fuses the methodologies

from Machine Learning (ML), Quantum Computing (QC) together forming a novel framework for enhancing system throughput and problem solution finding potential intelligently. Belonging to the broader realm of Artificial Intelligence (AI) technologies, machine learning (ML) leverages advanced computing capabilities and diverse algorithm approaches to address the challenges for application spanning areas lik chemistry,agriculture,hospitals, drug manufacturing, NLP, etc. Notwithstanding heritage in the processing of classical data, quantum machine learning processes the classical data by using the machine learning algorithm to explore the quantum phenomenon in the learning system. Quantum machine learning is a ground breaking change to computer science that may exponentially speed up the processing, as well as work with large, and complex datasets. This study emphasizes the contrasting aspects of classical and quantum machine learning and concludes with a discussion on the potential pathways and existing challenges in achieving quantum advantage through the framework of QML.

Rapidly increasing amounts of genomic information have made computational genomics a core field in the mapping of the genetic etiology of human disease. The algorithmic evolution and hardware acceleration techniques driving the next generation of disease gene discovery are synthesized in this review. We critically

examine the computational terrain according to three fundamental paradigms: Variant Prioritization, Polygenic Risk Analysis, AI-Powered Discovery, tackling head-on the central challenge of cross-ancestry portability. We then break down the computational complexity of these workflows and analyze how it can be implemented on the current hardware; we consider the GPUs as the leading hardware to accelerate them due to the high throughput and programmability. Lastly, we talk of how performance-portable, interpretable, and equitable computational frameworks need to be co-located in order to achieve the promise of precision medicine. The review summarizes the findings of 40 iconic publications to give an overview of the direction of computational problems in genomics in the future.

Machine learning (ML) has emerged as a transformative technology in oncology, offering significant promise for advancing chemotherapy design and individualized treatment strategies. By leveraging large-scale biological, genomic, and clinical datasets, ML models can accelerate drug discovery, predict therapeutic responses, and improve toxicity management. The integration of ML enhances precision medicine approaches and supports more patient-centered cancer care. However, challenges such as data scarcity, poor model interpretability, algorithmic bias, and limited cross-disciplinary collaboration hinder its full clinical adoption. This paper explores the current applications of ML in chemotherapy development, discusses the challenges in its integration, and highlights the potential impact on precision oncology and patient quality of life.

In Modern age all the matters and elements become digital and medical and healthcare system is not an exception. Advanced ICT and Computing in Healthcare results Digital Healthcare Systems for th advancement of healthcare and allied sector and industry. Initially only basic Information Technologies viz. Database, Networks, Multimedia, Networking, Software, etc. are considered as valuable and impactful, however in recent past other emerging technologies. Latest technological applications in healthcare brings several advantages and benefits of healthcare viz. speed, transparency, sharing, effective documentation, effective operations, instant and collaborated laboratory systems, etc. India is moving towards a digital economy supported by e-governance, m-governance, etc. and Digital Healthcare Systems may avail wider benefits with such additional benefits. Though there are ample advantages of Advanced ICT in Healthcare but though there are issues, challenges, and concern in Digital Healthcare viz. technological implementation and management, human resources, government norms and regulations, Data Susceptibility, Implementation loopholes, Cyber Security issues, etc. This work is a comprehensive one in regard to Digital Healthcare Systems including foundation, basic andamp; emerging technologies, and initiative in India, challenges and issues, including possible solutions.

Today there are emerging shifting of banking technologies can be observed from tools that assist us to systems that can actually think and act for themselves. This is the core of andldquo;Agentic AI.andrdquo; Itandrsquo;s a world beyond

todayandrsquo;s fraud alerts and chatbots. These AI agents can recognize complex circumstance, make a decision, and

then individually execute a multi-step plan to see it through. The dormant here is incredible. Imagine a wealth

manager that doesnandrsquo;t just stabilize your portfolio but acts as a full-time, personalized CFO, foresighted managing your taxes and savings goals based on life events. Or picture a loan process that is not just fast,

but immediate, with an AI individually underwriting your application by analyzing alternative data in real time. This is not just automation it is the creation of a truly intelligent financial partner. The handing over this level of independence is a huge jump. The biggest obstacle wonandrsquo;t be technical, but about faith and precept. How do we ensure these black box systems make decisions that are candid and interpretable, who is responsible when an AI makes a costly error? Banks and supervise will need to traverse this carefully. Agentic AI is commitment to metamorphose banks from mechanical service providers into pre-emptive, sharp architects of our financial lives. This Paper is dedicated on Agentic AI including its features, impact and role with reference to the emerging role in banking and financial sectors. Paper also highlighted the challenges and issues of Agentic AI in banking and financial sectors, effectively.

The worldwide higher education ecosystem is quickly evolving with the principles of Education 4.0 and Education 5.0, highlighting digital incorporation, adaptability, sustainability, and learner-centred approaches. Although undergraduate and postgraduate programs in India have increasingly embraced online and blended formats, doctoral education continues to be primarily conducted on campus. This inflexible framework generates a significant policy void that inhibits access for employed individuals, women, remote learners, and foreign academics, thus constraining inclusivity and sustainability. This study thoroughly analyzes the structural, institutional, and regulatory obstacles hindering the uptake of online doctoral education in India, specifically regarding the University Grants Commission (UGC) Ph.D. Regulations 2022. Based on global best practices, sustainability frameworks, and initial survey results from Indian professionals, the study posits that opposition to online doctoral programs is rooted more in obsolete regulatory frameworks and deeply ingrained institutional mindsets than in worries about academic rigor. The manuscript presents a staged policy framework for incorporating online doctoral programs in non-laboratory fields via strong quality assurance practices, supervisor training enhancement, digital research facilities, and clear assessment processes. It concludes that Indiaandrsquo;s goal to become a knowledgebased andldquo;Vishwa Guruandrdquo; relies on adopting adaptable, inclusive, and digitally supported doctoral education in accordance with Education 4.0 and 5.0 principles.

The rapid advancement of data analytics has significantly transformed Agricultural Financial Technology (Agri-FinTech) services, creating new opportunities for improving financial inclusion among smallholder farmers. This study conceptually examines the factors influencing the adoption of data-analytics-driven Agri-FinTech services in rural agricultural communities. The paper focuses on identifying technological, individual, and environmental determinants that affect farmersandrsquo; willingness to adopt digital financial platforms supported by advanced analytics. The study adopts a conceptual research design based on an extensive review of existing literature related to agricultural finance, financial technology, digital financial services, and technology adoption models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The findings indicate that digital literacy, perceived usefulness, trust in digital platforms, infrastructure readiness, and social influence are major determinants influencing the adoption of Agri-FinTech services. Data analytics plays a crucial role in enhancing credit assessment, crop insurance modelling, risk prediction, and farm decision-making through alternative data sources such as satellite imagery, mobile transactions, and weather data. However, adoption remains constrained due to inadequate digital skills, limited connectivity, concerns regarding data privacy, and high technology costs. The study highlights the need for improved rural digital infrastructure, farmer training programs, and transparent data governance policies to promote inclusive and sustainable agricultural financial systems.