The paper presents an AI-Driven IoT Refrigeration Monitoring (IRM) using support vector machine algorithms (SVM). The improvement in food safety and environmental sustainability has resulted in a paradigm shift in the techniques used for refrigeration. IRM guarantees that refrigeration units have perfect temperature management by seamlessly combining modern sensors, real-time data analysis, and artificial intelligence. The innovative strategy stops food spoiling, improves food safety, cuts down on waste, and promotes environmental responsibility across the supply chain. The intuitive alarm mechanism of the system notifies temperature variations as quickly as possible, which enables immediate remedial steps to be taken. IRM becomes a crucial instrument for preserving fresh foods and promoting environmentally aware behaviors since it bridges the traditional refrigeration rules with the digital world. The structure of the system, its benefits, and its potential to redefine industry norms in terms of safety and sustainability are discussed in depth in the article.

Recently developed AI-driven human-robot interaction (HRI) systems using RNNs, LSTM networks, and Transformer-based models like BERT and GPT, have shown the potential to enhance geriatric care. These technologies use sensor and IoT data to provide individualized, responsive, and effective assistance for elderly people. Many obstacles prevent these systems from reaching their full potential. Training data quality and representativeness are major issues. Collecting broad and reliable datasets that represent older people's real-world experiences is vital yet challenging. The ethical and technological challenges of protecting senior users' sensitive data are very high. To offer accurate and contextually relevant replies, multimodal data from aural, visual, and tactile sources must be seamlessly integrated. Maintaining robots' physical and cognitive ergonomics for natural and intuitive interactions is another design challenge. This study offers an AI-driven HRI system to improve senior care to solve these issues. Creating powerful AI models that can learn from high-quality, diversified datasets and combining multimodal data processing approaches to increase robotic caregiver accuracy and responsiveness are the goals. To employ the robots safely and ethically in geriatric care, the project will improve their physical design and apply strong data privacy and security safeguards. The suggested approach addresses these important difficulties to improve older well-being and independence by providing more individualized care and assistance.

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

One potential use of autoencoders in healthcare is the identification of anomalies in electrocardiogram (ECG) readings. The primary objective of this research is to design a reliable system that can detect irregularities in electrocardiogram data automatically. The goal is to improve the efficiency and accuracy of anomaly detection using autoencoders so that cardiovascular problems may be diagnosed and treated early. The objective is to prove that the suggested approach can correctly detect a variety of abnormalities, such as arrhythmias and other cardiac abnormalities, using electrocardiogram (ECG) data by conducting extensive experiments and validations. This study aims to provide a dependable tool for healthcare practitioners to evaluate ECG data effectively and quickly detect possible health issues, therefore contributing to the improvement of medical diagnostics. The goal of our effort is to make clinical decision-making in cardiac care settings more effective and to improve patient outcomes. The PTB Diagnostic ECG Database yielded five results, ranging from 0.57 to 0.92, for each of the five patients in the sample.

This research proposes using cloud-connected technologies and Gradient Boosting Machines (GBM) to improve smart Gasoline combustion efficiency. It uses real-time cloud data feeds to inform GBM decision-making to optimize fuel utilization and reduce emissions. A prediction model that adjusts to dynamic engine conditions was created using previous data and constant sensor inputs. It uses ensemble learning to optimize combustion parameters like spark timing and air-fuel ratio using GBM, improving engine performance. The experimental setup uses a cloud-connected smart petrol engine prototype with enhanced sensors and actuators. The engine and centralized computing system communicate seamlessly via the cloud architecture, allowing quick data analysis and model changes. Iterative model refinement and real-time modifications allow the system to adapt to changing operating conditions and maximize combustion efficiency. Compared to standard engine management systems, thorough calculations and practical testing reveal considerable fuel efficiency and pollution reductions. The cloud-connected GBM technique handles uncertainty and unexpected operating situations well, demonstrating its promise for smart engine systems. It advances intelligent engine control systems by using cloud connection and machine learning (ML) to optimize petrol engine combustion efficiency. The discoveries may help the car sector create environmentally friendly and high-performance engine solutions.

A sustainable project-driven supply chain (SPDSC) refers to integrating sustainability into project management to improve the sustainability performance of projects. Integrating sustainability and supply chain management is any project-based organization's most significant performance-related challenge. Therefore, the present study explores the drivers that can effectively tackle supply chain issues in managing projects undertaken in construction contracting firms for long-term sustainability. Sixteen critical drivers were considered through a rigorous literature review and fuzzy Delphi method (FDM) for construction contracting firms. Then, selected drivers were used to analyze established construction contracting firms using a grey relational analysis (GRA) technique. The results highlight that construction contracting firms (CCFs) considered the drivers of sustainable use of resources, energy and eco-efficiency, and environmental impact assessment; stakeholder engagement and social impact; and supply chain collaboration dimensions need to be improved for enhancing the sustainability of PDSCs. This study's contribution is twofold: It quantifies the identified set of critical drivers relevant to SPDSCs; it offers the CCFs a practical toolkit for sustainable project deliverables.

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.

Purpose: Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance. Design/methodology/approach: In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis. Findings: Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation. Practical implications: The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals. Originality/value: Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

COVID-19 has impacted the healthcare system across the globe. The study will span three pandemic waves in 2020, 2021, and 2022. The goal is to learn how the pandemic affects antenatal care (ANC) and emergency delivery care for pregnant women in Tamil Nadu, India, and how medical services respond. The study employs counterfactual analysis to evaluate the causal impact of the pandemic. A feedforward in combination with a simple auto-regressive neural network (AR-Net) is used to predict the daily number of calls for ambulance services (CAS). Three categories of the daily CAS count between January 2016 and December 2022 are utilised. The total CAS includes all types of medical emergencies; the second group pertains to planned ANC for high-risk pregnant women and the third group comprises CAS from pregnant women for medical emergencies. The second wave’s infection and mortality rates were up to six times higher than the first. The phases in wave-II, post-wave-II, wave-III, and post-wave-III experienced a significant increase in both total IFT (inter-facility transfer) and total non-IFT calls covering all emergencies relative to the counterfactual, as evidenced by reported effect sizes of 1 and a range of 0.65 to 0.85, respectively. This highlights overwhelmed health services. In Tamil Nadu, neither emergency prenatal care nor planned prenatal care was affected by the pandemic. In contrast, the increase in actual emergency-related IFT calls during wave-II, post-wave-II, wave-III, and post-wave-III was 62%, 160%, 141%, and 165%, respectively, relative to the counterfactual. During the same time periods, the mean daily CAS related to prenatal care increased by 47%, 51%, 38%, and 38%, respectively, compared to pre-pandemic levels. The expansion of ambulance services and increased awareness of these services during wave II and the ensuing phases of Covid-19 pandemic have enhanced emergency care delivery for all, including obstetric and neonatal cohorts.

Public-private partnerships are crucial for advancing agricultural sustainability and tackling issues related to enhancing global food security. They help make technology accessible to farmers so they can access markets. As PPPs bring together participants from the public, private, and civil society, they are commonly touted as a means of boosting productivity and fostering growth in the agriculture and food sectors. The PPP can assist in implementing cutting-edge technological breakthroughs and promoting private sector participation to reduce risks that could otherwise be excessive. PPPs are commonly understood as having the potential to modernize the agriculture industry and offer numerous concessions to help farmers achieve sustainable agricultural growth. The objective of the present study is to understand how these new partnerships are expected to play significant roles in identifying answers to the most important agricultural problems that Indian Agriculture is confronting. The study further aims to understand the critical issues in PPPs in the agriculture sector in the Indian context by analyzing their interrelationships and prioritization. The study utilizes Grey DEMATEL to determine these contextual relationships for PPP issues. Grey systems theory is a methodology that incorporates improbability and vagueness into the analysis. The critical issues in agricultural PPPs, as identified in the study, are “Complex and Time-Consuming Procedures”, “Governance Issues”, “Lack of Enabling Environment”, “Costly Contracting and Endogenous Contract Incompleteness”, and “Coordination Failures”. The present paper makes an effort to provide a detailed and exhaustive assessment of difficulties in agriculture PPPs in India, as well as a path forward for policymakers.

10 years ago in India, any retail focused on making the products available in the stores and help customers buy the same when they walked in. Today, with the availability of the Internet, smart phones, and tablets, the patterns of customer buying have changed. There are numerous apps and websites available to help customers find the right product, recommendations, offers and discounts at the click of a button anywhere, anytime. The boundaries of retail channels are eroding-a customer could be standing in a retail store and accessing information and offers available on a website. In the current scenario, shoppers are not just shopping online, but are merging their online and offline shopping practices. There are multiple channels available to customers and they, at a time, access one channel while in the middle of another channel, like accessing a mobile app from a store. Brick and mortar (B&M) retailers also face a continuous challenge from e-commerce retailers, who work with lower overhead costs and provide the best deals to customers on every purchase. Considering the paucity of literature and lack of adequate research on omni-channel in the Indian context, and emergence of implementing a true omni-channel strategy as a tool for the full integration of the offline and the online customer shopping experience, this study was undertaken to analyze the factors which influenced the purchase intent of the customer in omni-channel buying. Both qualitative and quantitative research was carried out to arrive at the variables, and hypotheses and statistical packages such as Excel and SPSS were employed to test the hypotheses. From this study, it emerged that in an omni-channel approach to consumer purchase intent by an Indian consumer, for it to be impactful, it must be supported by systems that make the order status visible to customers. There is likely to be a better buyer response if the product codes of the product across channels are the same. Further locationbased promotions positively impacted purchase intent. Companies would, therefore, benefit by adopting digital strategies that augment consistent product codes across channels, real time tracking of orders, and location based promotions based upon this research.