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).
