International Journal of Integrative Studies (IJIS)
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AI-Enhanced Digital Infrastructure Monitoring for Smart Transportation Systems: A Review
Get PDFSmart transport is a rapidly becoming a crucial part of modern urban transport, and is being facilitated by digital infrastructure in order to make it safer, more efficient and sustainable. However, the more complicated these systems are the more demanding are the needs in more sophisticated monitoring solutions. The Artificial Intelligence (AI) is a new solution in terms of improving the monitoring of the digital infrastructure through the real-time analysis of the data, supportive maintenance, anomaly detection and adaptive traffic control. Below is a review of literature and uses of AI in smart transportation systems monitoring. It examines the input of machine learning/computer vision and AI systems to the IoT to make decisions in advance, reduce downtimes, and improve passenger safety. The traffic flows optimization, structural health analysis of transport infrastructure, predicting vehicles maintenance, and the security of the transportation network are the most important ones. The evaluation compares and contemplates the advantages of AI-infused surveillance which ought to be enhanced in terms of efficiencies of operation, cost-saving, and sustainability. Other potential issues such as problem of scalability, interoperability, ethical concerns and data dependency on high quality are also critically outlined. The topic on discussion has research gaps, and the aspect of the future has been also touched upon in the paper; all this is connected with the concept of the simulation of stronger systems, enhanced with the assistance of edge AI, federated learning and digital twins. It appears that the findings imply that AI mediated surveillance is not a new technology, but rather a type of planning that will lead to intelligent, safe and sustainable transport systems
Drug Trafficking and Gender: The Invisible Role of Women as Victims and Agents
Get PDFDrug trafficking has been acknowledged as a universal vice that impacts socially, economically and politically in a destructive manner. Nevertheless, its gendered aspects are still under-investigated, especially the two-fold roles that females play in this criminal enterprise both as victims of exploitation and as participants in illegal activities. The use of women in drug activities is usually coerced, exploited or manipulated by poverty, gender inequality, and domestic violence, but women are also strategic participants, and some are compelled to be active because of their survival instinct or because of the marginalization of the system. The paper analyzes the interplay of drug trafficking and gender with special attention to how women are placed invisibly in positions that straddles the victimhood and the agency. Based on international reports, scholarly research, and case studies, the study will examine socio-economic and cultural reasons that force women into drug networks, stigma against them, and how this impacts families and communities. The paper states that women are disproportionately punished in the criminal justice systems, but their structural vulnerabilities are usually overlooked. The research ends with a recommendation of gender-sensitive policies, rehabilitation strategies, and the criminal justice reforms, which deal with the intricacy of the roles of women in drug trafficking
Deep Learning Approaches for Real-Time Medical Image Segmentation
Get PDFMedical image segmentation is an important feature of clinical diagnostics, surgical planning, and disease monitoring as it provides an opportunity to segment anatomy and pathological regions with high precision. By imaging modalities, tissue contrast, and noise, conventional image processing and machine learning approaches are known to have issues of image variability, although they are effective in specific situations. In the previous years, deep learning (DL) and convolutional neural networks (CNNs) specifically and its offshoots have revolutionized the medical image analysis by providing cutting-edge precision in segmentation across a great variety of modalities such as MRI, CT, PET, and ultrasound. The paper reviews development of the deep learning architecture, infrastructure, and implementation of deep learning models in real-time segmentation of medical images based on their performance in computation, generalization, and clinical utility. The architectures that are discussed in detail include U-Net, SegNet, DeepLabV3+, Attention U-Net, and Transformer-based (Swin-Unet, TransUNet) and their advantages and disadvantages. The model pruning, quantization and GPU acceleration are some of the optimization methods that the study has taken into the consideration to enhance the real-time performance. These problems as data scarcity, class imbalance, explainability, and new trends of federated learning and use of edge AI in medical imaging are also addressed. The findings indicate that real time high-precise segmentation currently becomes a reality with the integration of deep learning and highperformance computing systems and cloud based systems and has preconditioned the intelligent and automated clinical decision support systems
Partyfinder–Ai-Nlp Powered Event Discovery
Get PDFIn the modern era of Artificial Intelligence (AI) and Natural Language Processing (NLP), user interaction with technology has become more conversational and intuitive. Traditional event discovery applications rely on static filters and predefined keywords, limiting personalization. This paper presents PartyFinder, an AI-powered event discovery platform that understands user intent and emotions expressed in natural language to recommend suitable social events such as parties, concerts, and gatherings.The system integrates Flask for NLP processing, Node.js and Express for backend handling, and Firebase for real-time data synchronization. It leverages sentiment analysis, intent recognition, and entity extraction to process free-form user queries like “DJ night near me” or “quiet dinner for couples.” PartyFinder also includes an interactive chatbot interface, real-time mapping, andintegrated ticket booking for a seamless user experience. This AI-driven system bridges the gap between human language and event data, providing context-aware, dynamic, and personalized recommendations
The Transformation of E-Commerce: Artificial Intelligence and the Future of Digital Retail
Get PDFArtificial Intelligence (AI) is transforming the e-commerce industry, as it allows providing personalized experience, data-based insights, and operational efficiencies. The paper speculates the radical change that AI brought about in digital retail by analyzing 250 e-commerce customers and 50 entrepreneurs in India as primary data. This study considers a structured questionnaire to examine consumer attitudes toward AI-based personalization, trust, and purchase intention and the rates of adoption of AI tools by the business. Descriptive and inferential statistical analysis (mean, correlation, and regression) were conducted to establish the correlation between the adoption of AI and customer satisfaction. The results indicate a positive significant relationship between perceived convenience, satisfaction, and trust in online shopping and the use of AI. Nevertheless, issues related to privacy of data and bias are still present. The research finds out that although AI plays a critical role in competitive advantage, the ethical implementation and transparency are essential in maintaining long-term customer loyalty
Advanced Hybrid Perovskite Materials for High-Efficiency Solar Cells Under LowLight Conditions
Get PDFHybrid organic-inorganic perovskite solar cells (PSCs) have been shown to have impressive power conversion efficiencies under bright sunlight; but their operation under low-light is a crucial area of indoor photovoltaic and diffuse-light energy collection. This paper reports a systematic exploration on improved hybrid perovskite material which has been designed to achieve high photovoltaic efficiency in low-irradiance conditions. After using mixed-cation, mixed-halide perovskite structures along with defect passivation and interface optimization, the charge recombination losses had been reduced considerably. Performance of the device was measured over a very large spectrum of light intensities and has shown a better performance in terms of voltage retention, carrier lifetimes, and low-light power conversion efficiency than the traditional perovskite devices. The results provide material design principles of the next generation of perovskite solar cells that are adapted to work in low-light conditions (indoor electronics, smart sensors, building-integrated energy system, etc.)
Sustainable Urbanization: The Role of Smart Cities in Combating Climate Change
Get PDFClimate change has been hastened by the rapid urbanization where over half of world population is living in urban areas with the augmented emissions of greenhouse gases, energy use, garbage, and improper utilization of land. The solution to these problems has become smart cities: a transition towards automatization of digital technologies, green energy, sustainable transport and citizen-oriented government architecture to build self-directed and resilient urbanization. The following paper explains how smart cities will contribute to mitigating climate impacts and inclusive development. The successes reported in the global case studies in terms of emission reduction and resilience have been accompanied by the digital divide, threats to cybersecurity and high prices. The evidence shows that smart cities are pivotal to the achievement of the UN Sustainable development goals specifically sustainable cities and climate action. However, their utility is subject to the inclusive government, safety of information, and the related facts at the local level. The paper concludes that smart cities must go beyond technology and incorporate equity, participation and sustainability. Future research must address AI-powered climate modelling, circular economy modelling and nature-based technology, and how they can be improved to achieve sustainable urban futures
Microbial Degradation of Microplastics in Aquatic Ecosystems: A New Frontier in Environmental Bioremediation
Get PDFMicroplastics, which are defined as plastics that are below 5 mm in their dimension, have become widespread pollutants to the marine ecosystem, with dire consequences to marine creatures, ecological balance, as well as human health. The problem of accumulating synthetic polymers has become a burning issue all over the globe due to the recalcitration nature of these polymers. Microbial degradation has become a trend in recent years as one of the promising environmentally friendly methods to reduce microplastic pollution. Several bacteria, fungi and actinomycetes have shown themselves capable of colonizing, biofilm forming and enzyme degrading polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP) and polystyrene (PS). In this paper the authors discuss the processes of degradation of microbes, such as enzyme hydrolysis, oxidation, and fragmentation, as well as those environmental conditions that affect the degradation rate. Major microbial genera examined in the study included Pseudomonas, Bacillus, Rhodococcus, Aspergillus, and Ideonella sakaiensis whose enzymes are PETase and MHETase, which depolymerize plastics. Besides, the paper also assesses the possibility of using microbial consortia, genetic engineering and bioreactor-based methods to achieve large-scale bioremediation. The findings point to microbial degradation as a potential sustainable, low-cost alternative to physical and chemical degradation, although there are the problems of scalability and rate of degradation. The research arrives at the conclusion that the use of biotechnology and ecosystembased management to increase microbial activity can help in the world effort to alleviate plastic wastes to a greater extent
Verdant- Carbon Credit Estimation Tool using Satellite Data
Get PDFAccurate estimation of biomass and carbon stock is essential for monitoring ecosystem health, planning conservation activities, and evaluating carbon credit potential. This study presents a systematic analysis of vegetation health using Normalized Difference Vegetation Index (NDVI) and canopy cover data collected for the years 2017–2024. The proposed methodology integrates satellite-derived NDVI, canopy percentage, and a regression-based biomass model to estimate above-ground biomass, carbon stock, and carbon credits. Biomass was calculated using a vegetation index– driven formula, carbon stock was derived from biomass, and carbon credits were estimated based on carbon sequestration potential. Trend analysis, scatter plots, and growth computations demonstrate clear vegetation fluctuations over the study period. Results show increasing biomass and carbon stock until 2021, followed by a sharp decline corresponding to potential disturbances or land-use changes. These findings highlight the usefulness of NDVIbased remote sensing in rapid ecological assessment and carbon valuation
Impact of Personality Traits on Impulse Buying Behavior: Evidence from Pune, India
Get PDFThe impulse buying contribution in the overall consumer purchases contributes a huge percentage of consumer purchases and a study in the sphere of consumer behavior might be attractive to study. One of the issues that contribute to the tendency of consumers to make impulsive purchases is the impact of the psychological factors in particular the personality-related factors. The study compares dimensions of personality of Big Five in relation to Openness, Conscientiousness, extraversion, agribleness, and neuroticism with impulse buying behavior of consumers in Pune city in India. A total of 420 respondents took part in the study in order to collect primary data through structured surveys that were administered in malls, supermarkets, and on the internet. Multiple regression analysis indicated that Extraversion and Neuroticism were established as significant predictors of an increased number of impulse buying behavior and Conscientiousness is negatively correlated. The impact of openness and Agreeableness was not significant. The marketers can obtain the psychological insights provided by the findings that can be valuable in the formulation of the targeted retailing strategies as well as in the refinement of the insights into consumer impulsivity in the settings of emerging markets