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Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE. -
Recommender system for surplus stock clearance
Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend those which require clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
MVTamperBench: Evaluating Robustness of Vision-Language Models
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerability to adversarial tampering and manipulations remains under-explored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping; based on real-world visual tampering scenarios such as surveillance interference, social media content edits, and misinformation injection. MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks. This benchmark challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families. We reveal substantial variability in resilience across tampering types and show that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code, data, and benchmark to foster open research in trustworthy video understanding. 2025 Association for Computational Linguistics. -
Financial Analytics AI in Sustainable Innovations
Financial analytics integrates AI, ESG factors, and risk management to drive sustainable investments. It enables data-driven decision-making, optimizing financial and environmental outcomes. AI-powered tools like machine learning and predictive analytics enhance risk assessment and portfolio optimization. ESG integration ensures ethical and impactful investments. Despite challenges like data reliability, financial analytics is key to fostering a resilient, equitable, and sustainable global economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Survey on deep learning techniques used for object identification of underwater forward looking sonar images
Underwater object identification using forward-looking sonar (FLS) images is crucial for autonomous underwater vehicles (AUVs) for navigation and obstacle avoidance. Deep learning techniques have emerged as powerful tools for object recognition in various domains. This paper surveys deep learning approaches employed for object identification in FLS images. We examine the effectiveness of popular deep learning frameworks such as YOLOv5, EfficientDet, and MobileNet, and transfer learning, data enhancements to improve object recognition performance, and the role of adversaries training. We also examine the potential of focusing and lightweight CNN algorithms developed for FLS images despite these advances, challenges still exist due to the limited number of registered cases. The paper analyzes how deep learning methods address these challenges and highlights their effectiveness in object identification. We aim to provide a comprehensive overview of the current state-of-the-art in deep learning for FLS object identification, paving the way for further research and development in this field. Results of this study show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment. 2025 Author(s). -
Regulation and innovation in financial markets: The impact of fintech on traditional banking and financial systems blockchain technology
In this chapter, we present the underlying technical principles of distributed ledger technology (DLT) and blockchain technology and outline their practical applications in FinTech. In the recent years, DLT and blockchain technologies in general and cryptocurrencies, in particular, have attracted substantial attention from both researchers and practitioners due to their unique technological features such as the lack of centralized control and high level of anonymity. Because of the disruptive nature, DLT and blockchain have led to the evolution of decentralized applications in multiple domains such as finance, health care, supply chains etc. In this chapter, we first outline basic principles and foundations underpinning the DLT and blockchain technologies. Second, we discuss several applications in the FinTech domain such as cryptocurrencies, smart contracts, risk management, corporate finance, governance, crowdfunding, and derivative markets. 2025, IGI Global Scientific Publishing. All rights reserved. -
SharePort: A Cost Saving and Energy Efficient Ride-Sharing Application for Airport Commutes
SharePort is a mobile application specifically designed for finding people to share rides with, from airport locations. It allows users to find other users at the airport who are traveling to similar locations within a threshold of 2 K M s. This paper talks about the implementation of such an application, its benefit to the society in terms of individual costs, energy savings, traffic reduction, etc. It lays out the design patterns, features and their contribution to the overall idea. In addition to its various positive impacts on the individual expenditure of commuters and the environmental benefits, SharePort also resolves the issue of needing a third-party application to contact the people they are willing to share rides with, by integrating a chat feature, which enables ease of communication and accountability. Initial evaluations demonstrate that pairing up commuters can reduce ride costs by 30-50% per user depending on distance traveled. Additionally, a shared trip also decreases the number of vehicles used for overlapping commutes, reducing fuel burn and carbon emissions compared to independent commutes. Not only does it reduce individual trip costs but it also contributes to lower fuel usage, fewer vehicles on the road, and a more sustainable mobility ecosystem that mutually benefits commuters, cities, and the environment. Relevant SDGs: SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action). 2026 IEEE. -
Characteristics of Users Seeking Romantic Relationships on AI-Powered Dating Platforms and With AI Companions
AI now underpins nearly every major dating platform, influencing how people discover partners, express themselves, and form romantic bonds. This chapter explains why studying AI in romance has become essential, outlining how algorithmic matching, behavioural design, and personalisation shape digital relationships. It identifies key demographic groups, personality traits, and attachment styles that are drawn to AI-enhanced dating contexts, and examines their motivations, attitudes, and self-presentation patterns across various platforms. The chapter also explores the rise of artificial intimacy, showing how AI companions provide emotional safety, stability, and reinforcement that encourage ongoing engagement. Using psychological approaches, it discusses how users perceive AI as a romantic or supportive figure and how these perceptions interact with individual needs. Ultimately, the chapter highlights how user characteristics and AI design together drive the emergence of new forms of digital intimacy and their broader psychological and social implications. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Virtual influencers in Niche markets: Unlocking opportunities for targeted marketing
The need for an alternative approach to traditional influencer marketing is beginning to emerge due to the changing nature of the digital marketing environment where brands are starting to use Virtual Influencers (VIs) to target those consumers who are in unique and sometimes overlooked categories in particular niches. This chapter discusses how VIs provide the best opportunities to deliver very niche, first-party, data-driven content to narrow target constituencies of consumers. Since VIs have the power to shape exact personas that target audiences embrace or at least value - such as sustainability, luxury, tech, or subcultures - they can build emotionally touching experiences that regular marketing may overlook. The chapter focuses on how brands across the globe are implementing VIs in various sectors including sustainable fashion, luxury goods, innovative technology among others to portray how VIs positively affect industries reaching a niche but loyal audience. 2026, IGI Global Scientific Publishing. -
Effect of Work Experience on Psychological Capital and Job Satisfaction among Employees
In todays fast-paced workplaces, where technology is evolving at a dizzying rate, professionals face a myriad of problems. Their inability to strike a healthy work-life balance may lead to feelings of dissatisfaction with their job. Consequently, in order to achieve flexible, long-term growth and job happiness, businesses should support their employees good psychological development. Primary data was acquired from employees in the automotive manufacturing company, totalling 95 individuals, using standardized questionnaires that had a good level of reliability and validity. The results indicated that there is no significant effect of work experience on the psychological capital of employees (F = 1.21; p < 0.30) and their job satisfaction (F = 0.35; p < 0.70). The major findings indicate that regardless of an employees level of experience, there is no substantial variation in the psychological capital and job satisfaction of the employees. This variation may also arise because of other specific factors. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Integrating intelligence: The convergence of computer science and engineering in cyber-physical systems
The dynamic and innovative paradigm known as cyber physical systems (CPSs) arises from the merging of digital technology and physical infrastructure. This chapter provides a thorough analysis of CPSs, covering the basic ideas, constituent parts, a range of applications, and their integration with more complex subjects. Fundamentally, CPSs represent the smooth fusion of computational and physical components, enabling real-time control, analysis, and monitoring. The fundamentals of CPSs are explained in this chapter, with a focus on how they facilitate the development of interconnected networks that can coordinate complicated tasks across multiple domains. A close examination of the complex interactions that occur between sensors, actuators, processors, and communication networks in CPS designs demonstrates how these components work together to gather, process, and distribute data. Furthermore, a wide range of industries, including infrastructure, manufacturing, transportation, and healthcare, are impacted by the diverse applications of CPSs. CPSs transform conventional processes, improving efficiency, safety, and production. Examples of these processes include intelligent healthcare devices that monitor patient vitals and smart transportation systems that optimise traffic flow. When CPSs are combined with more complex subjects, they become even more powerful, accelerating innovation and change in a variety of fields. By enabling CPSs to process and analyse data at network edges, edge computing can lower latency and bandwidth consumption. Algorithms for machine learning improve decision-making, allowing CPSs to adjust and gain knowledge from real-world data. By protecting CPSs from cyberattacks, security and resilience measures guarantee the availability and integrity of vital systems. Furthermore, human CPS contact opens up new collaborative paradigms and gives people the ability to communicate with intelligent systems in a natural way. To sum up, this chapter gives readers a thorough grasp of CPSs and how they have revolutionised contemporary life. It adds to the continuing conversation on CPS research, innovation, and implementation by clarifying their basic ideas, elements, applications, and integration with more complex subjects. With ongoing research and cooperation, CPSs have the potential to completely transform our world and bring in a new era of intelligence, creativity, and connectivity. 2025 selection and editorial matter, Kamal Upreti, Nishant Kumar, Mohammad Shabbir Alam, Mohammad Shahnawaz Nasir and Debabrata Samanta; individual chapters, the contributors. -
Green Energy Harvesting using a Flexible Bio-triboelectric Nanogenerator
Bio-triboelectric nanogenerators (B-TENGs) show promise as a sustainable and renewable source for harvesting green energy using natural biocompatible and biodegradable substrates. Dry leaves contribute to a large amount of waste accumulation daily, even though they can be used to generate energy. Almost all the dry leaves collected during cleaning procedures are burned, resulting in greenhouse emissions and air pollution. This work aims to consider using biodegradable fresh and dry leaves as a bio-source for a cost-effective, sustainable, and flexible energy-harvesting system. When different frequencies and pressures were applied to B-TENGs, significant potential power output of around ~30V and ~350?W was produced. The experimental results and density functional theory (DFT) calculations support the charge transport phenomenon in dry-leaf powder under different compressive strains. A surface influences charge generation in B-TENGs and the presence of functional groups with inhomogeneous particle distribution, as demonstrated by experimental and mathematical modeling. The current work is ideal for large-scale manufacturing since it uses natural waste materials in dried forms, as well as simple and low-cost preparation. Therefore, our environmentally friendly solutions highlight the special abilities of plants to produce electricity for various flexible electronic applications. The Minerals, Metals & Materials Society 2025. -
Enhanced Approach for Precision Agriculture Using AI/ML Techniques
Precision-based agriculture has been made possible by recent technical breakthroughs and developments in information technology. These new developments have made it possible to better utilise contemporary methods and instruments, like IOT, soft computing, and wireless sensor technology, to increase the agricultural productions environmental and economic sustainability. Precision farming is a new trend in agriculture that sets itself apart from traditional farming methods by applying resources in a way that is efficient, planned, systematic, and justified in order to produce higher and better yields. Precision farming uses geographic information systems like weather patterns, remote sensing technologies like Wireless Sensor Networks (WSN), and soft computing tools like Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Decision Trees (DT) to monitor and predict farm produce requirements in real time and for the future. This study examines the application of several methods and tools used in precision farming. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Investigating Factors for an Inclusive Workforce for Women in the Logistics and Supply Chain Industry
This study seeks to identify and analyze the major factors that contribute to an inclusive workforce for women in the area of logistics and supply chain. It further addresses the need for gender diversity and inclusivity in a traditionally male-dominated field by adopting a human-centric approach. This study employs a combination of Fuzzy Delphi Method (FDM) and Fuzzy Best Worst Method (FBWM) for methodically identifying and prioritizing factors that influence inclusiveness for women in the logistics and supply chain industry. FDM gathers experts' opinions and achieves a consensus on the identified relevant factors. Subsequently, FBWM is used to analyze the factors, providing a clear priority ranking based on their relative significance. The analysis identified potential factors that are crucial for fostering an inclusive workforce in the logistics and supply chain industry for women. The factors were classified into three main categories: employee growth and culture, inclusive business ecosystems, and accessibility and diversity factors. Based on the global weights, the top three ranked factors are: gender-inclusive supply chain practices, skill development workshops, and supporting women-owned businesses. This study is original in terms of gender inclusiveness in the logistics and supply chain industry. The innovative combination of multiple methods stipulates a robust methodology for identifying and analyzing the factors that impact inclusiveness, offering a novel contribution to the literature and practical applications in this field. 2025 The Author(s). Corporate Social Responsibility and Environmental Management published by ERP Environment and John Wiley & Sons Ltd. -
Phytotoxins: Terrestrial Plant Sources
Plants produce a wide variety of phytochemicals during biochemical reactions. Plant secondary metabolites (PSMs) are specialised metabolites or phytochemicals that are produced by plants in response to biotic and abiotic stresses or as a by-product of metabolism. PSMs are needed by the cells of the plant to interact with its environment and are produced in very small concentrations. They have various therapeutic effects and are used as medication in various conditions. However, the difference between therapeutic and toxic levels of these compounds is so low that when administered in excess, they can cause adverse conditions. These toxic PSMs are also known as phytotoxins. Phytotoxins are highly versatile in their mode of toxicity, chemical composition, and structure. The major groups of phytotoxins that have been categorised are alkaloids, terpenoids, glycosides, non-protein amino acids, glucosinolates, cardenolides, tannins, phenolics, flavonoids, and phytoecdysteroids. These phytotoxins have several toxic effects like allergenic, pesticidal, hallucinogenic, and allelochemical and may even cause fatalities. The effects of these phytotoxins in humans range from the disturbance in the metabolic pathways taking place in various organs to adverse conditions like cytotoxicity, neurotoxicity, and carcinogenicity. This chapter discusses in detail the various categories of phytotoxic compounds from land plants and their major biological activities. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Reward Based Garbage Monitoring and Collection System Using Sensors
Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field. 2024 River Publishers. -
Still Waters Run Deep: Groundwater Contamination and Education Outcomes in India
We investigate the impact of groundwater contamination on educational outcomes in India. Our study leverages variations in the geographical coverage and timing of construction of safe government piped water schemes to identify the effects of exposure to contaminants. Using self-collected survey data from public schools in Assam, one of the most groundwater-contaminated regions in India, we find that prolonged exposure to unsafe groundwater is associated with increased school absenteeism, grade retention, and decreased test scores and Cumulative Grade Point Average (CGPA). To complement our findings and to study the effect of one such contaminant, arsenic, we use a large nationally representative household survey. Using variations in soil textures across districts as an instrument for arsenic concentration levels we find that exposure to arsenic beyond safe threshold levels is negatively associated with school attendance. 2024 Elsevier Ltd -
Revisiting cognitive assessment in the Indian prison setting
Purpose: Individuals with cognitive impairment are more likely to come into contact with the criminal justice system (Kimbell, 2016). Yet, only a handful of studies describe the nature of cognitive impairment experienced by inmates and the different types of challenges faced by researchers and clinicians while conducting cognitive assessments in correctional settings specifically in low-and middle-income countries. Design/methodology/approach: In the present paper, the authors describe different types of ethical and logistical challenges they faced while conducting cognitive assessments with inmates in India and suggest ways in which future researchers and clinicians could overcome them. Findings: Authors raise a discussion on the purpose, advantages, and limitations of psychological testing, highlighting alternative ways of cognitive assessment that may be more effective, resource-efficient, and sustainable. Originality/value: Implications for education and training in psychological assessment, forensic and clinical practice and policymaking are discussed. 2020, Emerald Publishing Limited. -
Examining psychometric properties of the Interpersonal Needs Questionnaire among college students in India
Background: With the second-highest population in the world, suicide-related deaths in India are high, and adults under 30 are particularly at an increased risk. However, empirical examinations of factors contributing to suicide in India and assessments of reliability and validity of self-report measures assessing these constructs are rare. Aims: The present study examined the psychometric properties of the Interpersonal Needs Questionnaire (INQ). Materials & Methods: Undergraduate students in India (N=432) completed the INQ and questionnaires assessing suicidal ideation, depression, fearlessness about death, and pain tolerance. Results: Confirmatory factor analyses of the 15-item INQ indicated that after removing three items assessing perceived burdensomeness, the two-factor structure of INQ demonstrated acceptable fit with good internal consistency for each of the subscales (?=.84.90). In line with the interpersonal-psychological theory of suicidal behavior (IPTS), thwarted belongingness and perceived burdensomeness interacted to predict suicidal ideation. Additionally, these constructs were positively associated with suicidal ideation and depression, and weakly correlated with fearlessness about death and pain tolerance. Discussion: Results support the relevance of the IPTS for understanding suicidal ideation among college students in India. Conclusion: The results suggest that modified INQ demonstrates strong internal consistency, as well as good construct, criterion, and discriminant validity among Indian college students. 2021 The American Association of Suicidology. -
Scripts About Happiness Among Urban Families in South India
The ways in which parents socialize positive emotions have important implications for youth wellbeing, though little is known about parental goals and responses to adolescents happiness in culturally diverse families. Using an open-ended qualitative methodology, we explored parent and adolescent views about situations leading to happiness, responses and justifications to the expression of happiness, and what parents would like to teach their children about happiness in a sample of 209 parent (56.3% fathers; Mage = 42.79years) and adolescent (85.2% girls, Mage = 14.95years) dyads in Bengaluru, India. When prompted to identify adolescents recent experiences of happiness, both parents and adolescents primarily described academic and extracurricular achievements, followed by special events and receipt of tangible items, social interactions, and overcoming difficult situations. The two most common parent responses to adolescents happiness were responding with appreciation or encouragement of the achievement and providing further instruction or advice, with fewer responses focusing on enhancing/maintaining the emotional state of happiness itself. A substantial proportion of participating parents reported that their child should focus on task improvement when feeling happy, followed by affect maintenance (i.e., the child should be happy), or express their emotion with restraint. The findings contribute to developing a culturally-informed understanding of socialization of happiness in diverse families. 2021, The Author(s), under exclusive licence to Springer Nature B.V.