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1st International Conference on Advanced Com.uting and Em.rging Technologies, ACET 2024

作者： Kant, Kam.l Pandey, Sarvesh Shanker, Udai M. M. M. University of Technology Department of Computer Science & Engineering Gorakhpur India Banaras Hindu University Computer Science - Mmv Varanasi India

ISBN: (纸本)9798350367720

The success of applying blockchain technology for transaction processing is based on a consensus m.chanism.to be utilized. The new solutions for the sam. are being reported and claim.ng to fix or overcom. it. However, the reported consensus protocols are not capable enough to serve the purpose adequately because the m.jority of widely used blockchain platform. purposefully exclude m.trics like delay and throughput, which are essential for the efficiency of consensus protocols. Therefore, this paper discusses blockchain fundam.ntals, consensus m.chanism., their variants, and our super node concept is discussed, and we perform.extensive experim.nts and our analysis prelim.nary results indicate that as the num.er of nodes increases, the throughput increases rapidly. © 2024 IEEE.

关键词： blockchain consensus decentralization perform.nce m.trics trade-offs

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Enhancing Hum.n Safety through m.desty Guard: A Com.rehensive Review of OpenCV-Based Surveillance System.

Enhancing Human Safety through Modesty Guard: A Comprehensiv...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Priya, P. Nithin Kum.r, D.A. Rithika, m. Sanjay, S. Jeyam.ry, J Sushm. M. Kumarasamy College of Engineering Computer Science and Engineering Karur India M. Kumarasamy College of Engineering Computer Science and Engineering India

ISBN: (纸本)9798331527518

The increasing deploym.nt of reconnaissance organizations in civic and isolated places has raised concerns about privacy, ethics, and cultural sensitivities, particularly when it com.s to the unintended capture of nudity or other im.odest im.gery. In diverse environm.nts where cultural norm. dictate specific standards of m.desty, there is a growing need for system. that can autom.tically detect and m.nage nudity in surveillance footage without infringing on individuals' rights or exposing sensitive m.terial unnecessarily. This system.utilizes artificial intelligence (AI) and computer vision to detect nudity in real-tim. video feeds, offering custom.zable responses such as blurring, alerting security personnel, or securely archiving sensitive footage. This paper outlines the technical architecture, deploym.nt strategies, and deep learning algorithm. of the m.desty Guard system. em.hasizing its potential applications in various environm.nts where surveillance and m.desty concerns intersect. Through the use of advanced AI and real-tim. video processing, the system.prom.tes ethical surveillance practices while respecting cultural diversity and privacy. © 2025 IEEE.

关键词： Deep neural networks

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A Review of m.ngo Leaf Diseases Classification, Causes and m.nagem.nt Strategies

A Review of Mango Leaf Diseases Classification, Causes and M...

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2023 International Conference on Advanced Com.uting and Com.unication Technologies, ICACCTech 2023

作者： Kum.r, Sandeep Gupta, Bhupesh Garg, Lucy M. M. Engineering College M. M. Deemed to Be University Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering Technology Department of Computer Science & Engineering Panipat India

ISBN: (纸本)9798350380880

The m.ngo, or m.ngifera indica, is a beloved tropical fruit that is well-known for its delicious flavor and nutritional benefits. However, because to the presence of illnesses that harm.both the leaves and fruits, m.ngo production confronts m.jor difficulties. This study paper exam.nes a wide range of pathogens and environm.ntal factors in a thorough investigation of m.ngo leaf and fruit diseases. The paper gives a thorough exam.nation of the detection, m.nagem.nt, and prevention of m.ngo diseases by a thorough assessm.nt of the literature and em.irical investigations. Our research offers light on the intricacies of som. of the m.jor factors that affect the health and productivity of m.ngo orchards, including fungal infections, bacterial illnesses, and abiotic stresses. The need of early identification and sustainable m.nagem.nt techniques is underem.hasized, highlighting how vital they are. © 2023 IEEE.

关键词： Diseases

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m.lti-m.dality Collaborative Recom.ender System.: An Overview of Techniques and Evaluation m.trics

Multi-modality Collaborative Recommender Systems: An Overvie...

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2023 International Conference on Advanced Com.uting and Com.unication Technologies, ICACCTech 2023

作者： Gupta, Sorabh Bindal, Am.t Kum.r Prasad, Devendra M. M. Engineering College M. M. Deemed to Be University Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering & Technology Department of Computer Science & Engineering Samalkha India

ISBN: (纸本)9798350380880

m.ltim.dal Collaborative Recom.endation System. (m.RecSys) have em.rged as essential solutions in the digital age, addressing the challenges posed by a vast array of online content and services. This paper offers an overview of the landscape, em.hasizing the significance of m.ltim.dal content while outlining prim.ry approaches. We categorize these m.dels based on their techniques, ranging from.traditional m.trix factorization to advanced deep learning configurations. A particular em.hasis is placed on m.ltim.dal data fusion, highlighting three m.thodologies: early, interm.diate, and late fusion. Additionally, we delve into evaluation m.trics vital for assessing system.perform.nce and discuss com.only used loss functions such as m.an square error (m.E), Bayesian Personalized Ranking (BPR) loss, and contrastive loss. m.reover, this study elucidates the role of self-supervised learning and pretraining in enhancing recom.endation results. This study outlines a coherent pipeline, assisting researchers in grasping the prevalent concepts and m.thodologies in the field. At its core, the progression of m.RecSys underscores the potential of leveraging diverse m.dalities to deliver m.re precise and personalized recom.endations in today's digital ecosystem. © 2023 IEEE.

关键词： Recom.ender system.

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Param.ter findings in Healthcare Analysis using K-m.ans and Decision Tree Algorithm 15

Parameter findings in Healthcare Analysis using K-means and ...

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15th International Conference on Advances in Com.uting, Control, and Telecom.unication Technologies, ACT 2024

作者： Hakkaladavar, Divya S. Khanai, Rajashri Department of Computer Science Engineering Dr. M. S. Sheshgiri College of Engineering and Technology Belgaum India Department of Computer Science Engineering KLE Dr. M. S. Sheshgiri College of Engineering and Technology Belgaum India

ISBN: (纸本)9798331300579

Our heart is one of the m.st fundam.ntal or significant parts of our body. The heart is responsible for m.intaining blood flow and form.ng it. There are thousands of cases on the planet connected to heart disease. A growing num.er of people are com.itting suicide because of coronary illness. Different side effects like chest pain, fast heartbeat, etc are referenced. The m.dical industry tracks down inform.tion. This paper discusses anticipating coronary illness using computer-aided calculations. Here, we will utilize different AI calculations like a help vector classifier, K-m.ans, and D-tree algorithm.. Calculations are used based on highlights and to anticipate coronary illness. By applying data m.ning techniques, it takes less tim. to predict disease. Based on the highlights, com.utations forecast cardiac illness. Using data m.ning techniques like D-tree and K-m.ans algorithm. speeds up and enhances disease prediction precision. © Grenze Scientific Society, 2024.

关键词： Heart

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Augm.nted Reality for Industrial m.intenance Using Deep Learning Techniques-A Review 2

Augmented Reality for Industrial Maintenance Using Deep Lear...

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2nd International Conference on Intelligent Data Com.unication Technologies and Internet of Things, IDCIoT 2024

作者： Sujanthi, S. Krishnha S, Rhythum M. Kumarasamy College of Engineering Computer Science and Engineering Karur India

ISBN: (纸本)9798350327533

Industry 4.0 represents the forefront of the ongoing industrial revolution, wherein the objective is to intricately m.rge the realm. of digital and physical dom.ins within industrial processes, with the ultim.te aim.of enhancing the efficacy of catering to the diverse requirem.nts of various stakeholders. This study presents a pioneering prototype of Pervasive Augm.nted Reality (AR), which has been devised in conjunction with esteem.d industrial collaborators. The prim.ry objective of this prototype is to facilitate the instantaneous m.nitoring of data and the identification of issues within an industrial assem.ly line. The prim.ry em.hasis lies within the preprocessing procedures, sem.ntic segm.ntation and object detection which enable the m.del to m.nifest as a virtual scene for various m.intenance-related endeavors, such as off-site m.intenance scheduling or revam.ing. A Hum.n-Centered Design (HCD) m.thodology was em.loyed to ascertain the intricacies, requisites, and predicam.nts faced by stakeholders, while concurrently establishing the prerequisites to steer the evolution of the m.del. The initial observations of these individuals are delineated, subsequent to an inaugural user study conducted within a factory shop floor setting, aim.d at assessing the proposed prototype and gathering input on potential enhancem.nts to optim.ze its efficacy within such contextual dom.ins. © 2024 IEEE.

关键词： Augm.nted Reality Deep Learning Hum.n Centered Design Industry 4.0 User Interface

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IoT and m.chine Learning for Efficient Truck Overload Detection and Prevention

IoT and Machine Learning for Efficient Truck Overload Detect...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Nandhakum.r, C. Priyanka, m.m. Raguna Devi, R. Santhiya, I. Shalini, K.m. M. Kumarasamy College of Engineering TamilNadu Karur India M. Kumarasamy College of Engineering Computer Science and Engineering TamilNadu Karur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Truck overloading occurs when a vehicle carries m.re weight than it is designed to handle. This practice is a significant safety hazard and can have detrim.ntal consequences for both drivers and road infrastructure. This research aim. to develop a robust and accurate m.del for predicting truck overloads, thereby enhancing road safety, reducing infrastructure dam.ge, and im.roving transportation efficiency. By effectively identifying overloaded trucks, we can prevent accidents, m.nim.ze environm.ntal im.act, and ensure com.liance with transportation regulations. It presents an integrated system.for real-tim. truck overload detection and alarm.ng. A Nodem.U- ESP8266 m.crocontroller acts as the system.s central processing unit, processing load data and initiating appropriate actions. By integrating m.bile devices and onboard technologies, fleet m.nagers and truck operators can receive tim.ly alerts and m.nitor truck loads, enhancing safety, efficiency, and sustainability in transportation operations. The proposed system.involves strategically placed load cells at checkpoints before flyovers. As trucks pass over the load cells, their weight is calculated, and the m.crocontroller processes the data. If the truck is found to be overloaded, a gateway to the flyover is closed, forcing the truck to take an alternative route as indicated by the vehicle's navigation system. This innovative approach effectively prevents overloaded trucks from.accessing flyovers, m.tigating potential risks and ensuring com.liance with weight regulations. © 2025 IEEE.

关键词： Fleet operations

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Exploring im.ge data association: A hybrid m.ning approach

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m.ltim.dia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kum.r Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for m.ning im.ge association rules is presented, which involves the fine-tuned CNN m.del, as well as the proposed FIAR and OFIAR algorithm.. Initially, the im.ge transactional database is generated using feature vectors obtained from.the fine-tuned CNN architecture. The proposed FIAR algorithm.is used to generate hash-indexed im.ge association rules, which are further optim.zed using the proposed OFIAR algorithm. This m.thodology com.ines the strengths of the CNN m.del to extract histogram.features from.im.ges, the FIAR algorithm.to efficiently m.ne frequent im.ge item.ets, and the OFIAR algorithm.to optim.ze im.ge association rules. The proposed m.thodology can be used to discover hidden relationships am.ng im.ges, leading to new insights in im.ge processing and analysis. Efficient results were obtained with a m.nim.m.support of 0.50 and a m.nim.m.confidence of 0.50. Experim.nts were perform.d on the fruits im.ge dataset consisting of 2618 im.ges from.six different classes, and the results show that im.ge m.ning is feasible and can produce strong optim.zed im.ge association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business m.dia, LLC, part of Springer Nature 2024.

关键词： Association rules

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Drop-Off System.Using m.chine Learning for Drivers 8th

Drop-Off System Using Machine Learning for Drivers

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8th International Conference on Inform.tion and Com.unication technology for Intelligent System., ICTIS 2024

作者： Priyanka, T. Gowtham.Dharm., E. Alexander, E.m. Kathiresh, P. Department Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

ISBN: (纸本)9789819766802

When fatigued or dwindling, drivers’ natural behaviors, perceptions, and awareness of their surroundings are com.rom.sed. Driving at night or under the influence of alcohol is an indication of this lack of caution am.ng drivers, which can result in collisions and pose a serious risk to individuals and the public. As a result, it is critical that the newest developm.nts in the autom.tive industry include driver aid system. that are capable of detecting driver fatigue and drowsiness. This project provides a nonintrusive prototype computer vision system.to track a driver's attention in real tim.. Eye tracking and groaning are two of the m.st crucial technologies for future driver assistance system. because allow for the interpretation of an operator's gaze, level of attention to detail, and level of fatigue based on their m.uth and eye m.vem.nts. Convolutional neural networks, com.only referred to as CNNs, are em.loyed in m.tion recognition and real-tim. tracking of eyes. A com.on problem.am.ng m.ny of the eye tracking m.thods proposed so far is that are sensitive to changes in illum.nation. This typically drastically lim.ts their potential for use in autom.tive applications. Real-tim. m.nitoring and detection of the eye and yawn are a topic of active research in the computer vision field. m.vem.nt positioning and eye tracking can be useful for facial alignm.nt. This paper presents an eye tracking and m.vem.nt detection technique that operates in dynam.c, real-world lighting conditions in real tim.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Autom.tive industry

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IoT-Based Sm.rt Farm.and Using Deep Learning 2

IoT-Based Smart Farmland Using Deep Learning

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2nd International Conference on Artificial Intelligence and m.chine Learning Applications, AIm.A 2024

作者： Um., J. Shobana, m. Abhiniti, G. Rahul, R. Kavinkum.r, S.P. Hirithik Roshan, S. M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India

ISBN: (纸本)9798350349221

The IoT based Sm.rt farm.and using deep learning is a system.for tracking anim.ls on agricultural land com.ines surveillance cam.ras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the anim.l's presence. Upon detection, the system.utilizes AI m.thods to categorize the anim.l type and send im.ediate Sm. notifications to farm.rs through a GSm.m.dule. It incorporates an anim.l-repellant buzzer and a gate control m.chanism. Through the use of transfer learning with CNN m.dels, it accurately identifies four specific anim.l species: elephants, cows, goats, and pigs. Continuous updates and training with new data m.intain its precision, serving the agricultural and wildlife protection dom.ins. This technology significantly aids farm.rs in safeguarding their crops, offering crucial insights into intrusions and am.lifying crop yield. Its prim.ry purpose revolves around sim.lifying the identification of anim.ls on agricultural land. © 2024 IEEE.

关键词： Crops

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