Authors - Syamanarayanaprabha S S, Abymon A, Aarathy K Sudhakaran Abstract - Digital transactions in India have increased significantly since the National Payments Coperation of India introduced the Unified Payments Interface (UPI) in 2016. This study focuses on Generation X (ages 45-60) in Kerala, a state known for its high level of digital literacy, whereas adoption studies have mostly focused on younger generations like Millennials and Gen Z. The results, which are based on responses from 132 participants, show that security, transaction speed and convenience of use are important element that promote UPI adoption within this demographic. However, technological difficulties and security-related worries still serve as obstacles. To increase UPI acceptance in this group, the study emphasizes the need of more user-friendly application designs, focused digital education initiatives, and calculated interventions.
Authors - Salvatore Vella, Fatima Hussain, Salah Sharieh, Murrium Zaheer, Alex Ferworn Abstract - Artificial Intelligence (AI) using Large Language Models (LLMs) is now used to automate simple knowledge worker tasks such as creating communications and summarizing documents. This paper presents a domain-specific language for creating agents, which we call ContentCreator (CC), to perform common knowledge worker tasks and mimic the behaviour of knowledge workers and their workflows. An interpreter has been built to execute and automate these workflows. In the same way that computer languages have progressed from assembly language to increasingly higher-level languages, CC enables the high-level programming of workflows that utilize large language models. CC is a simple language that non-programmers can use and understand. We use several use cases to demonstrate the ability of ContentCreator to automate workflows across multiple tasks.
Authors - Bryan Atunca, Henry Coico, Edgar Ramos, Araceli Macassi, Donovan Fuqua Abstract - The adoption of digital technologies and intelligent systems is significantly enhancing efficiency and safety across the global agri-food industry. Integrating Industry 4.0 objectives with circular economy principles offers effective solutions to challenges such as resource scarcity and environmental impact. This study proposes a leagile supply chain model—combining lean and agile method-ologies—to optimize mango supply chains worldwide. By leveraging Cyber-Physical Systems (CPS) in postharvest processes such as drying and storage, the model enables remote monitoring and control of key environmental parameters. CPS technologies, through sensors, actuators, and data-driven algorithms, provide real-time feedback, minimize human error, and support precision in decentralized processing units. Findings show a reduction in total supply chain time from 41 to 30 days (26.8%), with efficiency gains of 25% in harvesting and 28.5% in storage. The integration of CPS strengthens circular supply chain practices, reduces losses, and enhances sustainability, profitability, and system resilience across global mango supply chains.
Authors - Abhijit T. Somnathe, Parameshwari M, Sampurnima Pattem, Sridhar N Koka, Shokhida Abdurakhmanova, Pundru Chandra Shaker Reddy Abstract - Gold prices are non-linear, unexpected, volatile, and unregulated, making prediction difficult. Numerous studies have anticipated gold prices because they affect international economic and monetary systems. However, linear relationship studies rarely explain gold price changes. Gold price time series data is unpredictable, nonlinear, and volatile, making prediction difficult. Classical statistics and machine-learning (ML) approaches like Random-Forests, CNNs, and RNNs offer excellent accuracy but limits. A model that combines Temporal-Convolutional-Networks(TCN) with Query (Q) and Keys (K) attention-mechanisms (TCN-QV) is presented to improve gold price forecasts. The model extracts temporal properties from sequence data using stacked dilated causal convolution layers in the TCN architecture. To adapt weight distribution to information features, an attention mechanism is introduced. Finally, dense layers give projected results. This approach predicts Shanghai gold price time-series. The optimised model reduces Mean Absolute Error (MAE) by 5.47% in the least favorable case and 33.69% in the most favorable case across four investigational datasets compared to the baseline model.
Authors - Pooja Anil Patil, Yashodhara Haribhakta Abstract - Named Entity Recognition (NER) is a core task in Natural Language Processing (NLP) with applications in conversational AI, healthcare analytics, and search systems. It enables structured extraction of key entities from text, supporting clinical decision-making and automated diagnosis. While NER resources for high-resource languages like English are widely available, low-resource languages such as Marathi lack domain-specific datasets, particularly in the mental health sector. This work introduces ArogyaMINDNER, a Marathi mental health-specific NER dataset, designed to enhance entity recognition for diseases, symptoms, and treatments. The dataset is manually annotated using BIO tagging, ensuring linguistic accuracy and contextual consistency across formal and conversational text. Annotation policies account for psychiatric terminology nuances, accommodating Marathi’s morphological richness and linguistic diversity. To assess performance, ArogyaMIND-NER is benchmarked using classical machine learning models (SVM, Naïve Bayes, XGBoost, LightGBM, Random Forest), deep learning architectures (CNN, BiLSTM, BiLSTM-CRF), and transformer-based models (mBERT, XLMRoBERTa, IndicBERT, MahaBERT, MahaMarathi-7B). Comparative evaluation reveals MahaMarathi-7B and Marathi-Social-NER as topperforming models, achieving higher precision and recall in disease recognition, while classical models struggle with contextual understanding. This dataset and trained models establish benchmarks for Marathi NLP research, contributing to mental health analytics, clinical applications, and low-resource language processing. The findings advocate for finetuning transformer architectures for domain-specific NER, ensuring better entity identification in Marathi mental health texts. The dataset and models will be made available for further research and development.
Authors - Navin Barde, Deepak S. Sharma Abstract - Good E-Learning system for colleges would ensure student learning to be flexible, accessible, and interactive for being remote learning option in the Post COVID-19 times. The most important aspects are an intuitive and easy to use GUI to easily browse on any device, the interactive content such as video lectures, simulations, quizzes, forum to improve the engagement. Customized learning paths, progress tracking, grade management, and instant communication features enhance learning. We take security very seriously, we encrypt all of your data while storing it and ensure that students privacy is maintained with strong user authentication. Laying particular emphasis on usability, interactivity, personalization, and security, such systems create a convincing and flexible learning environment, which encourages life long learning.
Authors - Mohit Menghnani Abstract - The increasing expenses and shortage of qualified developers have driven many to look to software development with an eye toward the incorporation of Artificial Intelligence (AI). Full-stack development facilitates the web application process, from the interface to the server-side functionality that powers it. AI has changed the tasks for many software developments services, and full-stack is one of them. The integration of Generative AI (GAI) into full-stack applications presents a transformative opportunity for healthcare assistance by enhancing conversational user interfaces. This study explores the development of an AI-powered healthcare assistant using a React.js frontend, a Node.js/Express.js backend, and Google GAI for symptom analysis and response generation. The aim is to assess the feasibility, performance, and effectiveness of Generative AI in providing real-time medical suggestions. A process of repeating development occurs and the system continuously improves through testing alongside user engagement. Users can maintain safe communication between systems through Jason Web Token (JWT) authentication and HyperText Transfer Protocol Secure (HTTPS) encryption features in addition to multi-language compatibility. Test results show that symptoms processed by the AI-enabled chatbot require 1.5 to 2.5 seconds before providing recommendations with 85-90% accuracy which surpasses traditional rule-based chatbots. The obtained results indicate how GAI shows promise to expand healthcare availability to wider populations.
Authors - Aneri Pandya, Killol Pandya, Hemant Yadav Abstract - Convolutional Neural Networks (CNNs) play a crucial role in medical image analysis, particularly in the detection of pneumonia, by facilitating accurate diagnosis and treatment planning. This paper presents a robust approach to pneumonia classification using the publicly available RSNA Pneumonia Detection Challenge dataset. The proposed method employs an ensemble-based CNN framework that integrates multiple pre-trained architectures, specifically MobileNetV2, InceptionV3, and ResNet152V2, through a combination of stacking techniques and soft voting. This ensemble strategy aims to leverage the individual strengths of each model for improved performance. All models are trained and evaluated exclusively on the RSNA dataset, and their effectiveness is assessed using AUC, accuracy, and F1-score metrics. The results demonstrate that the ensemble consistently outperforms individual models in terms of prediction stability and classification accuracy. This work highlights the potential of deep model aggregation techniques to enhance diagnostic reliability in clinical pneumonia screening systems.
Authors - Sana S Santhosh, Ashitha Mohan, Vishnu N Abstract - Artificial Intelligence (AI) has transformed the cosmetics market by providing tailored suggestions based on client interests and behaviors. This study investigates the correlation between AI-driven personalization and consumer trust, emphasizing critical elements such as the precision of AI recommendations, transparency, and the degree of customization. Research indicates that AI-driven customisation markedly improves client trust, therefore influencing consumer pleasure and brand loyalty. Trust development in AI-driven recommendations is contingent upon perceived reliability, security, and transparency. The review emphasizes that AI transparency techniques, including explainable AI (XAI), enhance customer confidence, whereas excessive personalization may provoke privacy issues. Moreover, the precision of AI recommendations is strongly linked to consumer happiness and intents to repurchase, enhancing perceived expertise and diminishing decision-making anxiety. Brands utilizing AI-generated cosmetic advice must equilibrate customisation, openness, and security to cultivate enduring consumer trust. Future research ought to investigate the ethical ramifications of AI personalization and analyze the evolution of confidence in AI-driven cosmetic brands over time. With the ongoing advancement of AI technology, it is imperative to optimize algorithms for precision and ethical concerns to maintain client confidence in the beauty sector.
Authors - Amarnath K R, Nandini Nayakudi, Lekha S Nair Abstract - Accurate and timely identification of insect species is a foundational requirement for precision agriculture, ecological monitoring, and integrated pest management. This paper presents a novel deep learning architecture for fine-grained acoustic classification of insect species using multichannel wingbeat recordings. Targeting critical agricultural use cases, the model is designed to differentiate between two morphologically and acoustically similar pest species, Halyomorpha halys and Nezara viridula, and a beneficial pollinator, Episyrphus balteatus. The proposed approach utilizes log-Mel and Per-Channel Energy Normalization (PCEN) spectrograms as input features and integrates a residual convolutional backbone with Squeeze and Excite blocks and Transformer inspired self-attention modules to enhance spectro-temporal representation learning. Evaluation on a curated subset of the InsectSound1000 dataset demonstrates a classification accuracy of 88.73% and a macro averaged F1 score of 88.78%. Class wise analysis reveals that the model effectively captures subtle differences in wingbeat harmonics, achieving an AUC of 1.00 and AP of 0.99 for the pollinator class. The results suggest strong discriminative power across ecologically distinct classes and highlight the model’s potential for deployment in automated, non-invasive insect monitoring systems for real-time agricultural decision-making.
Authors - Nathalia Zacarias, Denys Huacanca, Edgar Ramos, Ines Tarazona, Donavan Fuqua Abstract - Supply Chain Planning in agri-food supply chains faces increasing challenges due to global disruptions and resource constraints. Supply Chain 5.0, driven by human-centered approaches, enhances resilience and sustainability by aligning dynamic demand strategies with technological advancements in uncertain environments. To address these complexities, this study applies the Best- Worst Method (BWM) to prioritize key strategic criteria across four dimensions, enabling consistent and structured decision-making. The results emphasize the relevance of advanced supply chain planning and human-centered innovation in fostering adaptive resilience and shaping a more sustainable supply chain. These findings support the transition toward resilient, sustainable, and human-driven agri-food supply chains aligned with the Supply Chain 5.0 framework.
Authors - Windy Permata Suyono, Marsellisa Nindito, Dwi Handarini, Hera Khairunnisa, Eka Septariana Puspa, Surya Anugrah, Rochma Sudiati, Rio Firnanda, Sabo Hermawan, Septi Nurmalita Abstract - The transformation of auditing through Artificial Intelligence (AI) has redefined the way auditors perform their tasks in the digital era. This study aims to examine the role of digital audit and auditor competence in enhancing audit effectiveness, with technological infrastructure as a moderating variable. Using a systematic literature review approach, findings show that both digital audit and auditor competence have a significant positive impact on audit effectiveness. However, technological infrastructure exhibits a dual moderating role. It negatively moderates the relationship between digital audit and audit effectiveness, suggesting that inadequate infrastructure may hinder the benefits of digitalization. Conversely, it positively moderates the relationship between auditor competence and audit effectiveness, indicating that skilled auditors perform better when supported by robust infrastructure. These findings emphasize the need for a holistic strategy that combines advanced technology, auditor upskilling, and infrastructure readiness. This study contributes to the body of knowledge on audit digitalization and supports Sustainable Development Goal 9 (SDG 9) by promoting industry innovation and resilient infrastructure.
Authors - Amita Verma, Simran Sankhyayan, Kritika Sheoran, Rukhsaar Dhaliwal Abstract - Artificial Intelligence has evolved the operation and functioning of every sector and banking is no exception to it. The inclusion of AI has revolutionized banking operations to a significant extent, following the introduction of Automated Teller Machines and Online Banking, transforming traditional banking practices in India. This contribution intends to unravel the complex yet pressing intersections between technology and Banking. This paper examines how AI-powered credit scoring systems, compared to conventional methods, are utilized by banks and financial institutions to accurately assess credit scores and predict credit risks. Credit scoring has been completely transformed by artificial intelligence, which offers sophisticated models and predictive analytics that promise increased efficiency and accuracy. This paper discusses the benefit of leveraging AI technology in credit scoring in terms of financial inclusion of underserved populations of the country paving the way for the establishment of an equitable society. Nevertheless, there are drawbacks and restrictions of using Algorithm technology in credit scoring. This paper highlights the main legal issues and challenges, with particular attention to issues with interpretability, data privacy, data breaches, etc. The principles of fairness, accountability, and transparency are invoked, and the possibility of unintentional biases is called into question by this lack of explainability in AI credit scoring tools. All such issues are then weighed in the context of the existing legal framework in India which eventually highlights the grey areas in the legal realm that the concerned authorities like the legislature, RBI, and financial institutions should ponder upon to ensure the balance between innovation and justice.
Authors - Howard Prioleau, Saurav K. Aryal, Jeremy Blackstone Abstract - This study contributes to ongoing efforts in agricultural technology by advancing the application of artificial intelligence in poultry disease classification, crucial for sustaining agriculture amidst global demographic changes. Focusing on the Chicken Disease Image Classification task, the research evaluates and finetunes state-of-the-art Convolutional Neural Networks (CNNs) like EfficientNet and ConvNeXT, alongside Vision Transformers (ViTs) such as DeiT and Swin Transformer. These models, pretrained on ImageNet, are adapted to classify various poultry diseases from chicken fecal matter images, representing conditions like Coccidiosis, Healthy, New Castle Disease, and Salmonella. The study juxtaposes these advanced models with traditional machine learning approaches to assess their relative efficacy in handling class imbalances and complexities inherent in the dataset. Through a comprehensive evaluation using Precision-Recall curves and Confusion Matrices, the research sheds light on the nuances of model performance in disease identification. The findings underscore the challenges posed by imbalanced datasets and emphasize the need for balanced data in training machine learning models for disease classification. This work showcases the capabilities of current state of the art AI in agricultural settings, aiming to enhance early disease detection tools for poultry health management, especially beneficial in resource-limited environments.
Authors - Sarvani Kocherlakota, Poojitha Panchakarla, Padmanabhuni Venkata Sai Rushikesh Vardhan, Vecha Guhana Naga Gayathri Abstract - E-commerce and supply chain management has emerged as a prominent research topic in recent decades, with numerous findings published in leading international journals. To investigate its current research landscape and emerging trends, we conduct a Bibliometric analysis by using R software 4.5.0, and VOSviewer to identify the research trend, emerging topics, and collaborations among countries, authors, documents and the contributing academic journals during the study period. Sustainability (Switzerland) journal is having the highest publications and Journal of Operations Management is having the most cited document. Zhang X is the most contributing author and Sun Y is the most cited author. China is the most contributing country with around 45% of total publications and also has high international collaborations. By research gap analysis, the future direction in the domain of supply chain management can be integration of live-streaming, emerging technologies, disruption risks, and consumer behavioral preferences.
Authors - Jaskaran Singh Saini, Dinesh Kumar Saini, Bhawna Chahar Abstract - Smart non-motorized transportation is crucial for the sustainable development of urban areas. An astronomical rise in the number of motorized vehicles, particularly in metropolitan cities, has compromised air quality and traffic conditions. NMT, as an effective mode of transport, is often neglected due to a lack of understanding of the long-term negative consequences of internal combustion engines (ICE) by policymakers and transport planners. The rising fuel costs and demand have inflated India's import bills and are expected to surge further until corrective measures are taken. NMT is a low-hanging fruit that can be implemented in urban hotspots at a relatively lower capital expenditure. It can help reduce the demand for fossil fuels and save the state enormous sums that would otherwise have been spent on capital-intensive carbon sequestration technologies. This study aims to highlight the significant economic benefits of NMT, based on data collected from 599 respondents in New Delhi and Chandigarh. The analysis reveals that, in addition to establishing the complex infrastructure of NMT, transport planners and policymakers must foster attitudinal change among citizens, i.e., the soft infrastructure.
Authors - Adithi Varma PC, Venugopal J S, Mayadevi Thampatty PC Abstract - This paper explores the intricate relationship between financial literacy and health insurance as key determinants in fostering financial inclusion among middle-aged class. By examining the mutual connections and co-influence between these concepts, the study aims to uncover how enhancing the health literacy to facilitate greater financial inclusion. Financial Literacy empowers individuals to make sound decisions regarding health insurance, risk management, healthcare cost, and insurance coverage. Conversely, Adequate health insurance which suits your health plan act as financial safety net, reducing huge health-related expenditures and promote financial stability. This study is conducted using a descriptive and analytical design, surveyed 101 respondents in Kozhikode District via convenient sampling through interview schedule. Data analysis involved mean scores, Chi-square, and t-tests. This research proposes that by integrating financial education with health literacy programs we can a create a synergistic effect that leading to an improved health outcome and enhanced financial well-being. This study will facilitate the development of comprehensive strategies that address the financial and health challenges faced by individuals and thereby promoting financial inclusion.
Authors - Selvamani K, Kanimozhi S, Muthusundar SK, Saranraj G, Jagadeesh K Abstract - With the advancement of quantum computing, conventional cryptographic approaches are losing strength against possible attacks, showing the necessity of secure alternatives such as lattice-based cryptography, especially for block chain and IoT implementations. Although lattice-based cryptography provides strong quantum resistance, its practical application is hindered by high computational overhead, intricate key management, and power consumption in resource-constrained IoT devices. Additionally, using it in block chain demands secure signature schemes and authentication protocols without compromising performance. Existing solutions compromise between security and efficiency at the expense of power consumption and higher latency. In an effort to solve these challenges, we present a more robust latticebased cryptographic scheme that enhances aggregate signatures for low communication overhead without compromising security. Our solution also provides a light-weight key exchange mechanism specific to IoT devices, enhancing scalability and energy consumption. Adaptive security elements are also included to counter potential post-quantum threats. Performance tests indicate that our framework substantially minimizes computational complexity while improving security compared to currently available alternatives. The findings attest to its capability to secure block chain-based IoT networks while providing efficient cryptographic operations. By overcoming the major shortcomings of lattice-based cryptography, our research provides a scalable, quantum-resistant solution for IoT and block chain application security in the post-quantum age.
Authors - Selvamani K, Kanimozhi S, Muthusundar SK, Saranraj S, Raju S Abstract - In the contemporary digital environment, organizations across all industries are increasingly dependent on online services to enhance convenience, streamline operations, and boost overall efficiency. While this digital transformation offers undeniable benefits, it simultaneously exposes users and institutions to significant cyber security threats. Malicious actors constantly seek out vulnerabilities in these systems, employing sophisticated methods to gain unauthorized access to sensitive data. One of the most alarming and effective tactics used by cybercriminals is social engineering. Rather than targeting technological flaws, social engineering exploits human behavior, manipulating individuals through deception, coercion, or the illusion of trust to bypass even the most advanced technical security controls. As traditional cyber security defenses such as multi-layered encryption, firewalls, and vulnerability patches grow more robust, attackers are shifting their focus to the human element, which is often the weakest link in the security chain. Unlike technical attacks that require in-depth knowledge of systems and code, social engineering preys on emotions like fear, urgency, curiosity, or helpfulness. These psychological manipulations make it easier for attackers to extract confidential data or financial information without triggering alarms. This paper explores the most common forms of Social Engineering (SE) attacks, including phishing, pretexting, baiting, and tailgating. It also delves into the psychological principles behind these tactics and outlines essential preventative strategies to mitigate the growing threat of social engineering in today’s cyber landscape.
Authors - Iroshan Aberathne, Don Kulasiri, Sandhya Samarasinghe Abstract - Manual techniques are not yet capable of tracking such tiny abnormalities in the early stages of AD due to the inherent complex nature of the structural changes in the brain. Therefore, we propose a novel model agnostic unsupervised feature selection algorithm called Prominent Feature Selection (PFS) to identify significant or vulnerable brain regions via volumetric measurements extracted from MRI images. Features selected by PFS are evaluated in two aspects. First, we compare the results with several other feature selection methods to assess the interpretability of the selected features against age and gender. The experimental results show that the PFS results better describe the structural changes in brain regions against age and gender. Secondly, binary class classification is performed to observe the discriminative power of the selected features against Cognitive Normal (CN) vs. Early Mild Cognitive Impairment (EMCI) subjects. Binary class classification performance measures demonstrate the significance of PFS. It has higher F1 score values despite a lower number of features compared to other feature selection methods. Since, PFS can identify and rank the most significant brain regions at early the stage of AD, diagnosis may become more specific. PFS can also be used as an effective feature selection technique not only to enhance the model performance but also to extend the model’s interpretability which would improve personalized evaluation in areas like eXplainable Artificial Intelligence (XAI).
Authors - Mohammad Nasar, Mohammad Abu Kausar, Md. Abu Nayyer Abstract - Smart farming technologies, particularly image-based crop disease detection systems, are significantly transforming agriculture by enabling farmers to respond promptly to emerging threats. However, deploying centralized AI models in remote or rural farming communities remains challenging due to limited internet connectivity, heterogeneous data sources, and privacy concerns. This paper presents a federated learning (FL) framework designed to support secure and privacy-preserving crop disease detection in decentralized agricultural environments. In the proposed approach, lightweight convolutional neural networks (CNNs) are trained locally on crop images captured by drones or field sensors, ensuring that sensitive data remains at the source. Instead of transmit-ting raw data, only model updates are shared with a central server, which aggregates them using the Federated Averaging (FedAvg) algorithm. To enhance security and model integrity, a cosine similarity-based filter is incorporated to detect and discard malicious or corrupted updates. Additionally, an attention-based heatmap module is introduced to visually highlight disease-affected regions in the crop images, improving system transparency and building user trust. Experimental evaluations on the PlantVillage dataset and real-world drone imagery of wheat and tomato crops demonstrate that the framework achieves an average 4.8% improvement in F1-score under non-IID data conditions com-pared to centralized models, alongside a 9.3% reduction in communication overhead at 50 nodes. This solution is well-suited for resource-constrained rural settings and contributes to scalable, intelligent, and sustainable agricultural digitization.
Authors - Makrand Kadam, Prashant Dhotre Abstract - Active Directory (AD), created by Microsoft, plays a central role in how most organizations manage user identities, access, and system permissions. Because of its importance in controlling access across enterprise networks, it’s a prime target for cyber attackers. This paper takes a closer look at how AD is structured, highlighting key vulnerabilities and common methods used by attackers—like stealing credentials, escalating privileges, using Pass-the-Hash, or creating Golden Tickets. It also examines real-life security breaches to show just how critical AD security has become. Beyond identifying threats, the paper outlines practical defenses such as improved auditing, network segmentation, hardening of domain controllers, and adopting zero trust models. By bringing together current research and best practices from the field, this study offers cybersecurity professionals and system administrators a clear, actionable guide to better protect their AD setups against today’s increasingly sophisticated threats.
Authors - Rajeshwari S B, Sajini G, Jagadish S Kallimani Abstract - In today's fast-paced world, people frequently experience high levels of emotional stress and struggle to access personalized mental health support. Traditional mental health services often face challenges such as limited accessibility and a generalized approach, which can overlook individual emotional nuances. This one-size-fits-all method may fail to address the specific needs of individuals. The need for more responsive and individualized mental health resources has never been greater, highlighting the importance of innovative solutions that offer contextually relevant and personalized care. The Srimad Bhagavad Gita, a timeless spiritual text, offers profound teachings on managing emotions, finding purpose and maintaining balance in life. This project aims to develop a Mental Health Conversational Assistant that provides users with motivational responses rooted in the wisdom of the Bhagavad Gita. This chatbot lever-ages NLP and NLU to detect both the emotional state and the intent behind user queries. It uses advanced techniques like emotion detection and semantic understanding to analyze user input and respond with relevant verses from the Bhagavad Gita. By integrating Hugging Face's emotion detection models and sentence-transformer embeddings, chatbot is capable of providing tailored, contextually relevant verses from Bhagavad Gita that match both the user’s emotions and the meaning of their queries.
Authors - Reena S. Satpute, Nura Muhammad Sani Abstract - Effective disease surveillance and outbreak response are high public health priorities, particularly in resource-limited healthcare systems facing significant challenges with data collection, predictive modeling, and coordinated intervention strategies. It hence proposes an integrated AI-driven framework responding to these challenges and strengthening the capacity of public health authorities to better manage infectious disease outbreaks. This framework will integrate real-time data from different sources such as electronic health records, syndromic surveillance, environmental indicators, and social media in a harmonious way into one centralized repository for the enablement of advanced predictive modeling techniques. It mainly comprises a disease forecasting model, an outbreak detection algorithm, and an intervention optimization tool integrated to provide early warning, accurate prediction, and data-driven decision support. An extensive performance evaluation of the framework was conducted using accuracy, sensitivity, specificity, and area under the ROC curve as some of the metrics. The results are very satisfactory, while the overall accuracy is 94.8% (95% CI: 93.2%-96.4%) with an AUC-ROC of 0.967 (95% CI: 0.951 - 0.983). Comparative performance analysis using state-of-the-art deep learning architectures further establishes the superiority of the proposed hybrid model. Transparency and explainability in the framework were guaranteed up to an extent that allows understanding the models and decision-making process by the public health authorities.
Authors - Adulfo R. Arevalo, Ronald L. Pancho Abstract - This study examines the forecasting performance of three prominent models— Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), and Random Forest (RF)—in predicting the short-term price movements of Bitcoin (BTC) and XRP using real daily closing price data from January to June 2024. The research aims to determine which model delivers the most accurate forecasts by applying each technique to actual market data and evaluating their predictive outputs using three standard accuracy metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Results show that ARIMA consistently outperformed both SVR and Random Forest in forecasting accuracy, recording the lowest average errors across all metrics for BTC and XRP. While SVR performed poorly in capturing XRP's volatility, Random Forest showed moderate results but lagged behind ARIMA. These findings highlight ARIMA's robustness and practicality as a forecasting tool for short-term cryptocurrency price movements. The study concludes with model-specific recommendations, encouraging the use of ARIMA for near-term price prediction and suggesting further exploration into hybrid and deep learning models for future research.
Authors - Rajeshwari S B, Jagadish S Kallimani Abstract - In the realm of human-computer interaction, the development of input devices accommodating diverse user needs is paramount. Traditional peripherals like mice and keyboards, though effective in typical settings, often falter in specialized conditions such as low-light environments. Addressing these constraints, this study introduces a novel gesture and voice-controlled mouse system. By leveraging real-time video and audio inputs, the system interprets user gestures and voice commands, eliminating reliance on conventional input tools. Additionally, it tackles unresolved interface design issues, striving to optimize user experience and broaden computing resource accessibility. Key to its functionality is the integration of MediaPipe for precise gesture recognition and natural language processing for voice command interpretation. Utilizing standard webcam and microphone hardware enables hands-free cursor control and command execution, particularly aiding users with mobility limitations. Methodologically, the system blends foundational theoretical principles with innovative design concepts for practical realization. Rigorous testing demonstrates a 90% accuracy rate in gesture recognition and a notable 60% accuracy in low-light conditions. Voice command interpretation achieves an impressive 95% accuracy across various accents and speech patterns, with a significant reduction in task completion time com-pared to conventional setups. This research underscores the potential of an affordable and adaptable technology, promising enhanced digital accessibility through ubiquitous webcam and microphone integration.
Authors - Thanh D. Nguyen, Anh T. N. Nguyen Abstract - This study examines how habitual behavior and users’ perceptions of security influence their intention to adopt and use e-wallet services. Building upon the original Technology Acceptance Model (TAM), the research integrates elements from both the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended version, UTAUT2. Data were collected from 265 valid respondents via an online survey and analyzed using reliability testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The findings reveal that perceived security, general privacy concerns, and habit all positively affect the intention to adopt e-wallets. Further-more, this intention significantly influences actual usage behavior.
Authors - Nureni Ayofe Azeez, Solomon Akinboro, Isiekwene Chinyere Chioma, Charles Van der Vyver, Awoseemo Babajide Abstract - The propagation of fake news in the digital age has raised momentous concerns about the integrity of information dissemination. This research aims to carry out a comparative assessment of machine learning algorithms for identifying fake news in social media. To achieve this, an attempt was made to collect a wide-ranging dataset of news articles, encompassing both real and fake sources. In the initial phase of the research, we preprocess the news data, extracting relevant features such as text content, metadata, and source credibility. Leveraging a wealth of labelled data, these models learn to differentiate between genuine and fake news articles, while also providing insights into the characteristics that contribute to their classification. In a world where information authenticity is paramount, the implementation of this system provides a valuable tool for discerning consumers of news and enables the swift identification of misinformation sources. The models were trained using four different datasets. Several machine learning algorithms were used for carrying out comparative assessment in terms of their efficiency and accuracy to detect fake news. Selected metrics were chosen for a comparative assessment. At the end of the experiments, for Dataset 1, the Passive Aggressive Algorithm had the highest accuracy of 0.8333, while for Dataset 2, the Passive Aggressive Algorithm had the highest accuracy of 0.914285. However, for Dataset 3, the AdaBoostClassifier Algorithm had the highest accuracy of 0.9142857. Furthermore, in Dataset 4, several algorithms which including Logistic Regression, Passive Aggressive, Ridge Classifier, SGD Classifier, AdaBoost Classifier, Bagging Classifier, Extra Tree Classifier, Gradient Classifier, Random Forest, NUSVC, Linear SVC, Decision Tree, BernoulliNB, and GaussianNB, all had the highest accuracy of 1.0000000. A bar chart was used to represent the results graphically, showing the minimum, maximum, and quartile range.
Authors - Akash Karale, Pratvina Talele Abstract - With the rapid advancement of deepfake audio technology, producing artificial voices that sound remarkably realistic is now simpler than ever. As this becomes available In addition to opening up new creative possibilities, it also poses grave security, disinformation, and fraud concerns. Although many of the deepfake audio detection tools available today are accurate, their application in real-time situations is limited by their high processing overhead. In this work, we introduce a Convolutional Neural Network (CNN)-based lightweight deepfake audio detection model. Our model can detect AI-generated speech accurately and with minimal processing demands by utilizing Mel-Spectrogram features and an effective CNN architecture. The model works well with high accuracy and low latency, according to tests on standard datasets, which makes it appropriate for real-world uses like media verification and voice authentication. Looking ahead, we aim to improve the model's ability to withstand adversarial attacks and adapt to new types of deepfake generation techniques. In the future, we would like to improve the model's robustness against hostile attacks and make it more flexible to accommodate new deepfake generation techniques
Authors - Ram Joshi, Rutuja Khedkar, Prema Sahane, Shantanu Totewad, Nikhil Tarate, Prajodeep Patil, Kedarnath Zunjare Abstract - Deepfakes are manipulated or AI-generated images and videos that appear authentic but are not. A decade ago, detecting deepfakes was relatively straightforward due to the limitations of the technology. However, recent advancements in AI, machine learning, and deep learning have made deepfakes nearly undetectable. Although this technology is often used for entertainment, it is increasingly being used for malicious purposes. The widespread availability of AI tools has further facilitated the creation of convincing deepfakes by fraudsters. Given that people tend to trust what they see and hear, they are particularly susceptible to the influence of realistic deepfakes.
