International Journal of Multidisciplinary Global Research
The International Journal of Multidisciplinary Global Research with Online ISSN 3107-4197 is a peer-reviewed, open-access academic journal dedicated to advancing innovative, cross-disciplinary research on global challenges. It provides a platform for scholars, practitioners, and thought leaders from diverse fields—social sciences, humanities, natural sciences, business, and technology—to exchange insights, foster collaboration, and contribute to sustainable solutions. This Journal encourages submissions that transcend traditional boundaries, promoting integrative approaches to complex issues such as climate change, global health, social equity, economic development, and digital transformation. The journal aims to address the interconnected nature of the problems and support a deeper understanding of emerging trends by publishing original research articles, case studies, and critical reviews. With a commitment to fostering inclusivity, this journal welcomes contributions from scholars worldwide, ensuring diverse perspectives that resonate across academia, policy-making, and industry. The three regular issues of the International Journal of Multidisciplinary Global Research could reflect thematic focuses or align with evolving global priorities yearly. Research and Emerging Trends This section focuses on groundbreaking research, new technologies, and innovative practices across disciplines that shape the future. Topics include digital transformation, artificial intelligence, biotech advancements, and novel interdisciplinary approaches. Global Challenges and Solutions This section addresses pressing global issues, including climate change, public health crises, geopolitical tensions, and sustainable development. It emphasizes actionable research and collaborative strategies to address societal, economic, and environmental challenges. Cultural Perspectives and Social Change This section highlights the role of culture, ethics, and social dynamics in a globalized world. It explores themes like social equity, migration, education, human rights, and the impact of global movements and promotes dialogue on cultural diversity and social transformation. These allow the journal to cover various topics while maintaining coherence and thematic focus throughout the year. Editorial Process and Submission Guidelines The journal follows a double-blind peer review process to ensure the highest quality of research and fairness. Submissions are evaluated for originality, relevance, methodological rigor, and clarity. Accepted manuscripts undergo copyediting to meet the journal's editorial standards. Publication Frequency: Four issues per year (March, June, September, and December) - (Quarterly). Open Access Policy: All published articles are available freely to maximize global reach and impact. Article Processing Charges (APC): This Journal maintains reasonable APCs to ensure worldwide accessibility for researchers. MS Word IJMGR's Article Template Download.
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Integration of Lean and Green Manufacturing for Effective Waste Reduction
The increasing demand for sustainable industrial practices has made the integration of lean and green manufacturing a critical approach to achieving operational efficiency while minimizing environmental impact. Lean manufacturing focuses on eliminating non-value-added activities to enhance productivity, whereas green manufacturing emphasizes reducing energy consumption, emissions, and material waste to promote eco-friendly processes. This paper explores a synergistic framework combining lean and green principles to achieve holistic waste reduction in manufacturing systems. Through process optimization, resource efficiency, and adoption of innovative technologies, industries can significantly reduce operational costs, enhance product quality, and align with global climate change mitigation goals. A case-based analysis demonstrates how integrating value stream mapping, energy audits, and life cycle assessment tools provides measurable improvements in waste reduction and sustainability performance. The study concludes that lean-green integration offers a scalable and practical solution for industries seeking to balance economic growth with environmental stewardship.
Vol. 2 No. 4a (Special Issue) (2025): ऊर्जा शाश्वतता आणि हवामान बदल आयोजक मूलभूत विज्ञान व मानवशास्त्रविभाग AICTE-VAANI ( द्विभाषिक - मराठी आणि इंग्रजी ) AICTE-VAANI NATIONAL CONFERENCE on SUSTAINABLE ENERGY MANAGEMENT IN THE CONTEXT WITH CLIMATE CHANGE (BILINGUAL MARATHI & English) Online ISSN: 3107-4197 and ISBN: 978-81-992021-2-2
Copyright (c) 2025 International Journal of Multidisciplinary Global Research https://creativecommons.org/licenses/by-nc-nd/4.0
Traditional Indian Knowledge Systems in Environmental Sustainability
Currently, the predominant global focus is on environmental conservation—preserving water, conserving power, and safeguarding natural resources. This research primarily focuses on the Indian Knowledge System in relation to environmental sustainability. The Sustainable Development Goals (SDG) emphasize and examine ancient history as a method for developmental growth and progress. IKS primarily concentrates on the Vedic Era. Indian knowledge systems endeavor to establish a connection with the foundations of our cultural heritage and customs. Currently, the Sustainable Development Goals (SDGs) are collaborating with the Indian Knowledge System, which integrates traditional wisdom with scientific methodology to foster growth and progress, ultimately leading to advancements in artificial intelligence from a comprehensive perspective. The Vedas and Upanishads possess profound significance that elucidates the nature of the universe, its operations, and its contribution to societal well-being.
Multi-Nozzle Pesticide Sprayer for Agriculture
In the agricultural sector, farms are producing more productively thanks to automated spraying systems. This has resolved the labor issue. However, things are different in a location like India, where automation in the agricultural sector is challenging and the majority of Indian farmers are not in good financial standing. Consequently, there are several uses for the manually operated pesticide sprayer under these circumstances. Small-scale farmers highly value manually lever-driven backpack sprayers due to their affordability, design, and versatility. Two kinds of sprays are utilized in agriculture in nations like India: 1. Pump operated by hand; 2. Pump operated by fuel. The primary drawback of the hand-operated spray pump is that the farmer who uses it cannot use it continuously for longer than five to six hours because of the pump's weight. On the other hand, the fuel-operated spray pump needs fuel, which is costly and difficult to come by in remote areas. In this study, a manually operated multi-nozzle pesticide sprayer pump model is proposed that will reduce farmer fatigue while spraying at maximum rate in the shortest amount of time without requiring fuel.
Influence of Synthesis Techniques on the Size of Nanomaterials: A Comprehensive Review, Mechanisms, and Future Prospects
Nanomaterials are widely used today because their small size gives them special physical, chemical, and biological properties. Particle size and shape play a major role, as they affect surface area, optical and magnetic behavior, catalytic activity, and biocompatibility. Therefore, controlling nanoparticle size is crucial for improving performance. This review summarizes major synthesis methods and how they affect nanoparticle size. Top-down physical methods like ball milling and laser ablation are easy and scalable, while bottom-up chemical methods such as sol–gel, co-precipitation, microemulsion, and hydrothermal synthesis allow better control over size and morphology. Green synthesis using plant extracts, microbes, or biomolecules is also discussed as an eco-friendly alternative. The review highlights how different synthesis routes influence particle size, current applications, challenges in consistency and large-scale production, and future directions for creating uniform and sustainable nanomaterials.
A Review on the Effect of Transition and Rare Earth Metal Doping on the Color Change Property of Nanoparticles Used in Sensors
Colorimetric sensing using nanoparticles has gained immense attention as a simple, rapid, and cost-effective analytical technique due to its capability for naked-eye detection without the need for sophisticated instrumentation. The incorporation of transition and rare earth metal dopants into nanoparticles has emerged as a strategic approach to tailor their structural, electronic, and optical properties, thereby enhancing their sensitivity and selectivity toward diverse analytes. Doping not only modifies the electronic band structure and defect states of the host nanomaterials but also influences particle size, morphology, and surface chemistry, which collectively govern their optical behavior. This review provides a comprehensive overview of the role of metal doping in modulating the colorimetric responses of nanoparticles under various environmental and chemical stimuli. Particular emphasis is given to the classification of dopant types, host nanoparticle systems (such as metal oxides, noble metals, and semiconductor nanomaterials), and their specific sensing applications, including detection of toxic gases, heavy metals, biomolecules, and pH variations. Furthermore, the mechanistic aspects of how doping alters localized surface plasmon resonance (LSPR), energy band transitions, and charge-transfer processes are critically analyzed. The review also highlights recent advances, challenges, and future prospects of doped nanoparticle-based colorimetric sensing platforms in the context of real-world applications. By systematically correlating doping strategies with particle size modulation and resultant optical properties, this study underscores the pivotal role of doped nanomaterials as next-generation colorimetric sensors with high sensitivity, selectivity, and potential for portable, point-of-care diagnostics.
Magnetic Nanomaterials for Energy-Efficient Power and Distribution Transformers: Advances, Mechanisms, and Future Prospects
The growing global need for energy-efficient electrical power systems has driven significant research into transformer core materials that minimize energy losses. Although conventional silicon steel cores are widely used and reliable, they face limitations in reducing hysteresis and eddy current losses, especially during no-load or light-load conditions. Recent developments in magnetic nanomaterials—such as nanocrystalline soft magnetic alloys, amorphous alloys, and magnetic nanofluids—offer promising alternatives for improving transformer performance. These materials exhibit excellent magnetic characteristics, including high saturation flux density, low coercivity, high permeability, and lower core losses. This review provides an in-depth analysis of their magnetic behavior, synthesis techniques, relationships between structure and properties, and practical applications in power and distribution transformers. The factors contributing to their enhanced performance, such as suppression of eddy currents, improved magnetic flux conduction, and better thermal management, are explored. Additionally, challenges related to fabrication, cost, mechanical robustness, and long-term reliability are discussed, along with potential approaches and future research directions to facilitate their broader adoption in energy-efficient transformer technologies.
Building Information Modelling Approach for the Design of a G+7 Residential Tower Using Autodesk Revit
This paper explains how a G+7 residential building was planned, modeled, and structurally detailed using Autodesk Revit. The main goal is to show how Building Information Modelling (BIM) helps make design work more accurate, reduces mistakes, and improves teamwork during construction. The model includes both architectural and structural parts, created according to IS codes. The study shows how BIM makes the whole design process easier—from the first concept to detailed structural analysis—while also improving coordination between different disciplines. Structural components like beams, columns, slabs, and foundations were all created in Revit. The results show that using BIM helps reduce design errors, saves time, and makes construction smoother. Overall, this work supports the idea that BIM is a complete and effective method for modern structural engineering.
Gesture Recognition translator using Machine Learning, Computer Vision, & MediaPipe
Hand gesture recognition technology is transforming how humans interact with computers, especially in contactless interfaces and assistive communication. This paper presents the design and evaluation of a real-time hand gesture recognition system that combines Google's MediaPipe for landmark detection with classic machine learning on robust geometric features. The method strategically selects only critical, distance-based features between the wrist and fingertips and from the thumb to the other fingertips, resulting in a lightweight yet highly accurate classifier. Supporting five distinct static gestures—hello, good, yes, no, and thank you—the system achieves near-perfect recognition under typical webcam lighting. The paper details all stages, from dataset creation to live testing, and discusses plans for expanding gesture vocabulary and integrating audio feedback for multimodal interaction.
Rainwater Harvesting and Groundwater Recharge: A Case Study of Nagpur
Rainwater harvesting (RWH) and groundwater recharge (GWR) are crucial methods for the conservation of water resources and for mitigating the adverse effects of climate change. This research investigates the feasibility of implementing RWH and GWR systems to enhance local water supplies, decrease stormwater runoff, and ultimately improve groundwater levels. The study emphasizes the multiple benefits these techniques offer, including greater water security, reduced instances of water scarcity, and improved ecosystem health. It also examines various established and emerging technologies utilized for both RWH and GWR, such as rooftop harvesting, the collection of surface runoff, and the use of recharge wells. The results of this investigation confirm the efficacy of RWH and GWR in fostering sustainable water management practices and building water resilience within communities. The paper concludes that these methods are essential for tackling water scarcity and promoting water conservation, particularly in areas facing significant limitations in their water resources.
A Review of AI-Driven Early Detection Systems for Chronic Diseases
Artificial intelligence (AI)-powered systems for the initially recognition of chronic diseases are fundamentally revolutionizing health protection by facilitating prompt diagnoses, highly personalized treatment plans, and improved patient outcomes. These cutting-edge systems leverage advanced machine learning methods, including predictive analytics and deep learning, to analyze massive volumes of health data, such as electronic health records, medical imagery, and genomic information. By identifying subtle risk factors and complex patterns, AI can predict the onset of long-term conditions like cardiovascular diseases, diabetes, and neurodegenerative disorders well before a patient experiences symptoms, thereby enabling proactive management. This review focuses on the core principles, the wide array of data sources, and the algorithmic approaches that drive these detection systems. It also examines their clinical applications in real-world settings and provides a critical assessment of the major operational, ethical, and technical hurdles, such as data privacy, algorithmic bias, and the “black box” problem, that currently impede their widespread adoption. The paper concludes by looking ahead at emerging technologies, such as Explainable AI (XAI) and multimodal data fusion, and their potential to create a future where healthcare is more proactive, personalized, and equitable.
Enhancing Library Management with Software Solutions: A Comprehensive Approach to Efficient Resource Management and User Experience
Libraries have long been at the heart of academic, professional, and community learning, serving as repositories of knowledge and gateways to intellectual growth. However, the rapid expansion of digital resources, evolving user demands, and the integration of advanced technologies have posed new challenges for traditional library management systems. The reliance on manual operations or outdated software often leads to inefficiencies, limited accessibility, poor user experience, and underutilization of resources. To overcome these challenges, this paper proposes a comprehensive approach to enhancing library management through modern software solutions that prioritize efficiency, scalability, interoperability, and user satisfaction. The abstract outlines the objectives, methodologies, outcomes, and contributions of this study, while emphasizing the role of innovative technologies in transforming libraries into dynamic, user-centric knowledge hubs. The first focus of this research lies in identifying the gaps in existing library management practices. Many libraries continue to struggle with cataloging, inventory tracking, digital content integration, and user engagement.
Secure Communication Protocols for Wide-Area Smart Grid Systems
The modernization of power networks into smart grids has redefined conventional centralized energy systems into decentralized, intelligent, and highly interconnected infrastructures. This transformation is primarily driven by the integration of advanced communication technologies, Internet of Things (IoT) devices, Artificial Intelligence (AI)-based analytics, and automated control mechanisms. While these innovations enhance grid efficiency, resilience, and adaptability, they also expose the communication backbone to a diverse range of cyber threats. Wide-area communication infrastructures in particular are susceptible to data interception, unauthorized access, denial-of-service (DoS) attacks, and sophisticated advanced persistent threats (APTs) capable of targeting critical energy assets. To address these challenges, this study proposes the design and implementation of secure communication protocols tailored for wide-area smart grid environments. The framework introduces a layered security model that incorporates lightweight cryptographic methods, blockchain-supported data integrity mechanisms, AI-powered intrusion detection systems (IDS), and post- quantum cryptographic schemes to safeguard future grid operations. The proposed solution is structured around core security principles, including confidentiality, integrity, availability, authentication, and non-repudiation. Conventional security mechanisms, while useful in limited and isolated deployments, are insufficient for geographically distributed smart grids due to interoperability issues, latency constraints, and the computational limitations of IoT devices. To overcome these barriers, the protocol employs elliptic curve cryptography (ECC) and optimized symmetric-key encryption to achieve robust protection with low overhead, making it practical for resource-constrained environments. A private blockchain infrastructure is integrated to provide immutable and verifiable records of energy transactions, system configurations, and control commands, ensuring transparency and accountability across distributed nodes. parallel, AI-driven anomaly detection techniques are embedded within the system to enhance real-time monitoring and resilience against evolving cyber threats. These models, built using deep neural networks (DNNs) and recurrent neural networks (RNNs), are trained on large-scale datasets of smart grid traffic to recognize both known and zero-day attack patterns. This adaptive security approach allows for dynamic countermeasures and improved situational awareness in complex operating conditions. Furthermore, recognizing the long-term risk posed by quantum computing, the framework integrates post-quantum key exchange algorithms to safeguard communications against cryptographic attacks that could compromise classical encryption schemes in the future. Alongside its security advancements, the research also emphasizes performance optimization. A balanced evaluation of encryption strength, processing overhead, and communication latency was conducted to ensure compliance with industry requirements. Simulation results confirm that the proposed protocols deliver high levels of security while maintaining end-to-end latency well below standard thresholds, even under heavy communication loads. A case study involving a simulated wide-area blackout triggered by a coordinated cyberattack was performed to assess resilience. Results demonstrated that the integrated defense mechanisms— blockchain-backed data integrity, AI-based intrusion detection, and quantum-resistant cryptography—were able to mitigate the impact of the attack by more than 85%, ensuring stable grid operations and minimizing service disruption.
Review Paper on Livestock Health Monitoring and Disease Prediction System Using IoT
The integration of Internet of Things (IoT) technology in livestock management has revolutionized traditional farming practices, offering unprecedented opportunities for real-time health monitoring and disease prediction. This review paper examines the current state of IoT-based livestock health monitoring systems, analyzing their components, methodologies, and applications in disease prediction. The paper synthesizes research findings from 2018– 2024, highlighting the effectiveness of sensor networks, data analytics, and machine learning algorithms in early disease detection. Key findings indicate that IoT-enabled systems can reduce livestock mortality by 15–25% and improve overall farm productivity by 20–30%. However, challenges including data privacy, system interoperability, and cost-effectiveness remain significant barriers to widespread adoption. This review provides insights into emerging trends, technological innovations, and future research directions in precision livestock farming.
Machine Learning for Genetic Marker Identification in Rare Neurological Disorders: A Comprehensive Review for Early Diagnosis
Millions of people worldwide suffer from rare neurological conditions, which can be difficult to diagnose because of their varied clinical presentations and our incomplete knowledge of the underlying genetic pathways. The identification of genetic markers linked to these illnesses has been made possible by machine learning (ML) techniques, which may lead to an earlier and more precise diagnosis. This comprehensive review examines current ML methodologies applied to genetic marker identification in rare neurological disorders, evaluating their effectiveness, limitations, and clinical implications. We analyze various ML algorithms, including deep learning, ensemble methods, and feature selection techniques, discussing their applications in genomic data analysis, variant classification, and phenotype-genotype correlation. Along with emphasizing current developments and potential future paths in the discipline, the review also tackles issues including data scarcity, class imbalance, and interpretability.
The realm of sports is experiencing a dramatic transformation, driven by the assimilation of extensive data analysis. The enthusiasm from both scholars and industry professionals regarding the utilization of big data analytics (BDA) in athletics has surged significantly, leading to heightened demand for innovative research, development, and exploration of new methodologies. This review introduces a framework that encapsulates the current literature on the applications and impact of analytics within the sports sector. The studies evaluated in this paper utilize a variety of methods to assess and enhance athlete performance, including the creation of a player ranking system, visual assessments to pinpoint movement patterns, and the examination of numerical data during critical moments of the match. This review explores how big data analytics is revolutionizing player performance assessment, injury mitigation, training enhancement, and fan interaction. Key research publications are analyzed to offer a comprehensive perspective on the advantages and limitations of big data analytics in athletics. Additionally, potential future research directions are highlighted, emphasizing the significant opportunities within this field.
Object Detection Employing YOLOv8 in Conjunction with a Custom Dataset
In a variety of applications, including surveillance and safety systems, object detection is a very important characteristic. The objective of this study is to apply the sophisticated object identification model known as YOLOv8 to a particular dataset that has been developed for emergency collision avoidance systems specifically. For the purpose of conforming to the format specifications of YOLOv8, the dataset was painstakingly hand-labeled and polished. It included components such as autos, pedestrians, and barriers in a variety of situations. In order to improve the efficiency of the model, supplementary data and transfer learning strategies were applied. According to the results of the experiments, the model achieves a mean average precision (mAP) of [0.43], which demonstrates both its accuracy and efficiency. The ability of the device to detect potential threats in real-time may make it possible for it to be included in complete safety systems. For the purpose of optimizing real-time processing, additional modifications will be investigated in subsequent research, and the concept will be applied to edge devices.
Transforming Education through Cloud Technologies: A Review of Accessibility Challenges and Solutions
Cloud-based solutions have emerged as key instruments for transforming education worldwide. As cloud computing technology keeps growing, more institutions offer courses through these means for their teaching and learning process. The scalability, flexibility, and affordability of this technology ensure that more people can access it to achieve education. This paper discusses how these cloud-based platforms are shaping the future of education, based on their advantages and challenges, and the implications on the development of an interactive, personalized, and collaborative learning environment. Further, it addresses the security, privacy, and infrastructure challenges related to the adoption of cloud solutions in education and talks about the future of education in a cloud-centric world. The introduction of cloud-based solutions is creating a revolution in education across the globe in newer and exciting ways. Increasingly, cloud computing technologies offer alternatives to the way in which businesses and students learn. Such technologies are providing increased education options through affordability, scalable operations, and flexibility. This research endeavors to explore the transformative essence of cloud platforms towards shaping the future of education, throwing light on their merits and demerits along with implications in making learning interactive, personalized and collaborative. Apart from that, it addresses creation of cloud services implementation in education in conjunction with security and privacy aspects. Finally, the impact of cloud computing may change the dimensions of education in terms of its wide adoption.
Automated Interview Scoring (AIS) by Integrating Wordnet with NLTK
Manual rating of autobiographical interviews, the essence of the assessment of autobiographical memory in psychology, is a labour-intensive and time-consuming task. Here, narrative details have to be rated by human raters, that is, classified as internal (episodic) or external (non-episodic), which limits the scalability of such research efforts critically. This project looks at the application of NLP methods for automating the rating process. The objective of this research is to fine-tune a language model such as distilBERT toward the end of making its usage more efficient and relieving part of the burden from researchers without compromising the precision in content classification. The project further assesses the reliability of the automated system across various datasets and determines whether its general structure may present possible futures for advancing research in autobiographical memory. All these developments work towards focusing on efficient and scalable NLP tools development for mainstream psychological use.
Equity by Design: Harnessing AI TRiSM for Socially Responsible Innovation
The rapid expansion of artificial intelligence (AI) into socially sensitive domains such as healthcare, education, energy, and finance has intensified calls for innovation that foregrounds equity, accountability, and public trust. This article advances the framework of "Equity by Design," demonstrating how AI TRiSM—Trust, Risk, and Security Management—can be strategically harnessed to centre social responsibility throughout the lifecycle of AI systems. Synthesizing findings across human-computer interaction, policy studies, and algorithmic ethics, we argue that achieving truly equitable AI requires more than technical post-processing or regulatory compliance; it demands intentional, participatory design processes that embed trustworthiness, stakeholder engagement, and risk mitigation from inception. Drawing from empirical research, we examine how dimensions of trust—spanning statistical reliability, explainability, and user-centred design—directly influence the acceptance, calibration, and societal impact of AI technologies. We further elaborate on the necessity of preemptively identifying and addressing ethical risks such as bias, discrimination, and technological uncertainty through comprehensive socio-technical assessments, highlighting mechanisms by which risk governance can avert social harms and amplify AI’s positive outcomes. The article presents cross-sectoral case analyses to illustrate actionable pathways for operationalizing AI TRiSM, including participatory auditing, transparent explanation interfaces, and adaptive governance strategies tailored to diverse social and regulatory contexts. Ultimately, we contend that "Equity by Design"—anchored in the rigorous application of AI TRiSM—constitutes a transformative paradigm for innovation, empowering organizations and policymakers to embed justice, inclusivity, and sustainable trustworthiness into the fabric of emerging AI systems. By bridging technical rigor with social responsibility, this approach offers a roadmap toward accountable and equitable AI that advances the collective well-being of historically underrepresented communities and society at large.
Advanced In-Depth Literature Review on Intrusion Detection Systems
As cyber protective systems become more advanced, they rely on constant intrusion monitoring and tackling access, attacks, and associated renewing dangers to disguise malicious activities within the network. Modern cybersecurity designs would not be complete without intrusion detection systems (IDS), which monitor networks for suspicious activity and new threats. Three groups are examined in this work regarding IDS transformation: signature-based detection, anomaly-based detection, and hybrid models involving AI and ML. Federalized learning, blockchain integration, and zero-trust architectures are some of the new areas of research that are being thoroughly discussed. Other topics covered include the underlying methodologies, the integration of advanced AI/ML techniques, technical challenges like scalability and encrypted traffic analysis, and emerging trends in the field. The review draws upon diverse sources from IEEE, ACM, Springer, IOPscience, and ScienceDirect.
The Impact of Artificial Intelligence on Cultural Practices and Communication
Artificial Intelligence has emerged as a transformational force that dramatically affects communication and cultural behaviors in modern society. This article analyzes the complex impact of AI on how individuals and groups communicate, share information, and construct cultural narratives. AI-driven technologies, including natural language processing, machine learning algorithms, and generative AI, have changed communication by enabling personalized content distribution, real-time language translation, and automated responses. These progressions boost accessibility yet modify how individuals communicate interpersonally and in huge numbers. The way it carries crucial cultural consequences, AI curation based on liking creates echo chambers that alter consumption patterns of culture and also encourage other biases. At the same time, AI-generated art, music, and literature challenge the nature of creativity and authorship and what constitutes authentic and valuable human versus machine-generated cultural artifacts. Therefore, this significantly influences reshaping cultural identities and practices as AI provides an opportunity to save endangered languages, digitize cultural heritage, and facilitate global cultural exchanges. This will raise ethical questions, such as the commodification of cultural heritage, erosion of privacy, and a possible loss of human agency in creative processes when AI becomes integrated into cultural domains. Therefore, this paper takes an interdisciplinary approach by incorporating communication theory, cultural studies, and AI ethics to critically assess the opportunities and challenges posed by AI. Based on case studies and empirical analysis, it has demonstrated how AI both empowers and disrupts communication and cultural spheres. Therefore, the outcome of this research underlines that AI should be taken up in such a way that technological advancement goes hand in hand with ethics and cultural preservation. It is through this trip that the course of the transformation of the relationship between technology, communication, and culture within the digital realm will be much better understood.
Enhancing Digital Identity Verification and Content Rights Management with AI and Blockchain Using SVMs
This research explores merging AI-driven identity confirmation with blockchain mechanisms for rights management in digitized media—the proposed framework leverages supervisor learning techniques to authenticate digitized content and ensure secure ownership confirmation. Utilizing blockchain offers a decentralized and tamper-proof framework for managing rights, enabling transparency and trust among stakeholders. Supervisor learning algorithms, such as Support Vectors Machines, are applied to improve content authentication accuracy, ensuring that digitized assets are protected against unauthorized use or duplication. The combination of AI and blockchain automates the confirmation process and provides a reliable framework for managing ownership rights and licensing in a secure, distributed environment. This strategy holds significant potential for addressing the challenges of digitized rights management while fostering innovation and trust in digitized content ecosystems.
From the Consumers' Perspective: Plastic Free Consumer Products, based on a High Critical-Crisis-based Recommender System for the Manufacturers/Producers of Consumable Products packed with all types of containers based on Plastic Material
In recent years, the issues on the usage of plastic products from the consumer products industry and the generated plastic waste (PW) after use and PW management (PWM) from various domains of businesses and the human society caused the biological ecosystem (BES) and the green ecosystem (GES) to reach its imbalance state. This imbalance in the GES and BES causes various human hygiene problems, leading to and affecting the natural resources available to the next generation of living organisms from genuine accessibility. As humans, our foremost duty is to enjoy the natural resources from the available GES and BES, maintain them, and hand over the same for the next generation to utilize and train the successor generation to maintain fair practice. In such a scenario, the PW in human society is caused by the considerable percentage of business handlers of consumable products packed with all types of containers based on plastic material. The awareness and law enforcement for the usage of plastic exists in all nations, and the practice of the manufacturers of consumable products which reaches in massive volume, the common public is not provided with strict conditions to not to use such plastic on their products, which is the primary source of common public's PW generation. This article aims to provide a recommender system to the manufacturers/producers of consumable products (M/PCP) packed with all types of containers based on plastic material. In the name of a High Critical-Crisis-based Recommender System (HCCRS) for the Plastic Free Consumer Products (PFCP) to make the PW-free environment for the common public, where ordinary people are trained to handle the PW efficiently. This PFCP- HCCRS recommends clean and transparent procedures to manufacturers/producers to manage their consumer products for the market with solid and environment-friendly containers with green business promotion.
The Campus Pay: Offline Payment System Using RFID Technology for Campus Transactions
The Campus Pay system integrates RFID-enabled ID cards to allow for cashless payments on campus. This approach meets the need for safe offline transactions in domains with unpredictable internet availability. After the RFID enabled cards save transactions, when connectivity is restored transactions are then synchronized with a cloud based system. The paper describes the architecture, design and implementation of Campus Pay, focusing on how RFID is integrated with current infrastructure and how cloud data analysis is used in transaction monitoring.
Copyright (c) 2024 International Journal of Multidisciplinary Global Research https://creativecommons.org/licenses/by-nc-nd/4.0
Advancing Stock Price Forecasting: Exploring Methods, Models & Literature
In stock market prediction, aim is to identify future values of different financial stocks of companies. This research paper delves into the dynamic and challenging domain of stock price forecasting, that aims to Participate to existing body of knowledge by introducing a comprehensive analysis of methods, model building flow, and pertinent literature. By introducing the situation, this section emphasizes the difficulties posed by the intrinsic variability of financial markets as well as the crucial role that precise stock price forecasts play in financial decision-making. A key component of financial market is stock price forecasting that offers experts in finance & investors able to take wise investment decisions. This research paper explores the latest advancements, methodologies, and challenges in stock price forecasting. The paper systematically explores various forecasting methods, ranging from traditional time series analysis to advanced machine learning techniques. Understanding each method's advantages and disadvantages is emphasized, providing readers with a nuanced perspective on the applicability of different approaches in different market conditions.
YOLO-Based Framework for Detecting Suspicious Activities in ATMs
The YOLO (You Only Look Once) algorithm is the basis of this research's novel approach to ATM security. After that, we suggest a deep learning model for real-time suspicious activity identification utilizing LO (Look Once) techniques. The proposed framework allows our system to detect and monitor security risks in an ATM environment using YOLOv5's sophisticated object detection. It has a detection accuracy of 94.3% and analyzes video in real-time for surveillance purposes. This suspicious activity includes cash trapping, card skimming, and strange human movements. Forty ATMs were used to showcase the technology. Executing at a rate of 30 frames per second with an average accuracy of 0.91. These findings show that conventional monitoring has made great strides forward. Better technique sensitivity is also provided by high detection, which has a false positive rate of just 0.4%.
The Applications of AI in Smart Technologies and Manufacturing
Smart manufacturing has been driven forward by intelligentization, a major industrial production trend over the last several decades, with the help of AI technology. The modernization of AI by various modern businesses has given birth to a novel concept known as Industrial Artificial Intelligence (IAI), which serves as the technological basis for smart manufacturing. Artificial intelligence (AI) driven manufacturing improves several parts of closed-loop production chains, from production processes to product delivery. Considerably, the field of production monitoring has profited from IAI's incorporation of domain expertise. Modern artificial intelligence techniques, including adversarial training, transfer learning, and deep neural networks, are widely used in manufacturing for diagnostic and predictive maintenance purposes. IAI is widely believed to be a crucial technology that will propel industrial production forward in the future. Artificial intelligence (AI) driven manufacturing and its monitoring applications are thoroughly reviewed in this study. More precisely, it summarizes the primary IAI technologies and discusses their everyday use cases concerning the three primary production monitoring facets of problem detection, residual useful life forecast, and quality inspection. The current issues and potential avenues for IAI research are also covered. By incorporating them into the overview, this study further presents the papers on Monitoring with AI in Smart Manufacturing in this targeted part.
As hacker eye is a concept of significant risk to internet safety, hacker eye tries to deprive gullible consumers of private information. Phishing websites are the threat that researchers are trying to fight and various techniques in detecting phishing websites have been developed such as machine learning algorithms. Many phishing and legitimate websites can be applied to machine learning algorithm training to learn patterns and characteristics of sites to identify which are phishing and which are not. Once identified and blocked, these algorithms can then be used to find and prey on phishing Web sites, while users are still unscathed. A machine learning approach to detecting Hacker Eye website phishing websites is feature extraction where, through a set of features regarding a website such as URL structure, domain age, content, the ideas around these features are used for identifying such websites. Using deep learning algorithms to automatically extract features and identify intricate patterns in the company's website data is the alternative strategy. Overall machine learning based phishing website detection techniques are promising since such techniques are able to obtain accuracies that rival or even surpass rule based methods. However, and with further research and development, these techniques could become a powerful weapon in the arsenal of anyone fighting against online phishing attacks.
Chain Craft: Crafting the Future of Supply Chains with IoT and Blockchain Brilliance
Blockchain technology and the Internet of Things (IoT) are coming together to bring about a new paradigm in supply chain management that is more transparent, efficient, and secure. It mulls over the revolutionary effects of integrating supply chain blockchain technology with data from Internet of Things sensors. Organizations may optimize their logistical operations, get end-to-end visibility, and use Blockchain for immutable records. IoT devices capture data in real-time. Their synergy powers automated smart contracts, reduces intermediaries, and neuters risks of fraud and counterfeiting. This integration makes demand forecasting more accurate and inventory management and quality control more effective. Yet, due to the growing complexity and globalization of supply chains, IoT and Blockchain solutions are becoming viable to transform supply chains into resilient, adaptive, and sustainable ones. This paper analyses the key advantages, issues, and potential for these advanced technologies in supply chain operations.
DNA and KAMLA Approaches in Metamorphic Cryptography: An Evaluation
Metamorphic cryptography, a shift in the paradigm of cryptographic techniques, is a programming paradigm in which transformations that maintain the semantic meaning of the data transform the syntactic representation of it. This paper delves into two prominent techniques within metamorphic cryptography: KAMLA and DNA based cryptography. DNA based cryptography depends on the inherent properties of DNA molecules to encrypt and decrypt the data, providing a promise for more secure information and less computationally demanding schemes. In contrast, KAMLA transforms the original data in a series of mathematical transformations thereby making it resistant to different attacks. This research attempts an indepth review of these two approaches, highlighting their strengths and weaknesses and their applicability in different cryptographic applications. We aim to gain insights into the potential impact of these primitives in the cryptography of tomorrow by analyzing their security properties and computational complexity, and their practical feasibility. We also discuss possible ways to further improve on these approaches via a hybrid combination of DNA based and KAMLA techniques to enable enhanced security and flexibility.