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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Tyagi, Noopur Singh, Jaiteg Singh, Saravjeet Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Applications Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665490580

In logistics and transportation, for the administration and real-time monitoring of the process chain. There are a variety of approaches for following persons and things inside a specific area. One of them is the Global Positioning System (GPS), which allows objects on the ground to be geolocated as long as there is a passable line of sight to four or more satellites. GPS cannot be used in interior conditions as a result of this constraint. A few Indoor Positioning Systems (IPS) based on various technologies have recently been developed. This paper aims to review and summarize the existing technologies such as Radio Frequency Identification (RFID), Wireless- Fidelity (Wi-Fi), and Bluetooth Low Energy (BLE) positioning systems for indoor navigation and discuss the challenges, benefits and challenges of an IPS. Fusing the technologies can improve performance and provide fast and accurate navigation for mobile robots. © 2022 IEEE.

关键词： Indoor positioning systems

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The New Energy Demand Equation Response Mechanisms for Consumers and Prosumers 7

The New Energy Demand Equation Response Mechanisms for Consu...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Asha, V. Kumar, G. Kiran Mohammad, Q. Kansal, Lavish Begum, Rowsonara Gupta, Navya New Horizon College of Engineering Department of Computer Applications Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad50043 India Hilla University College Babylon Iraq Lovely Professional University Phagwara India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

A new energy management tool IDRPO is integrated demand response and prosumer optimization. IDRPO uses pro-consumer grid integration, consumer-centered energy management, and green energy allocation methods to stabilize the grid, boost energy efficiency, and empower energy users. This combination approach solves the current energy demand equation's evolving issues. Method 1 maximizes energy consumption via consumer-centric energy management (CCEM). CCEM stabilizes the grid and saves money with flexible demand, real-time pricing, and consumer options. CCEM's mathematical models help make sensible decisions like using energy off-peak and joining demand response programs. The Prosumer System Integration Model (PGIM) in method 2 determines if integrating prosumers to the energy system is feasible and inexpensive. PGIM determines customer assistance based on grid dependability, surplus energy, and power usage. This approach balances grid reliability and energy production. Method 3 shows how to transfer green energy to the grid using REAA. To ensure the project is cost-effective and environmentally friendly, REAA calculates the EII and CBR. The options reduce pollutants and boost the economy, improving green energy. The IDRPO's strategy promotes grid safety, low prices, long-term solutions, consumer choice, and energy flexibility. It claims to solve all energy sector issues by respecting the law and improving the market. © 2024 IEEE.

关键词： Demand response

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Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

Advancements in Crown Sheath Rot Detection using DenseNet121...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thilagavathy, R. Kumar, S.Senthil Deebalakshmi, R. Deepa, D. Kavitha, R. Kumar, L. Sathish Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India School of Arts and Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India Vit Bhopal University School of Computing Science and Engineering MP Kotrikalan India

ISBN: (纸本)9798350348910

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease. © 2023 IEEE.

关键词： Integration

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Monitoring Students in Online Learning Environments Using Deep Learning Approach 3

Monitoring Students in Online Learning Environments Using De...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Dudhmal, Ritu Khatik, Irfan Kadam, Sachin Choudhary, Surbhi Zurange, Sachin Borate, Vishal Pimpri Chinchwad University Dept of Computer Engineering Maval Pune India Yashwantrao Mohite College of Arts Science and Commerce Bharati Vidyapeeth Pune India Institute of Management and Entrepreneurship Development Bharati Vidyapeeth Department of Computer Applications Pune India Pes College Ec branch Dept of Computer Science Engineering Banglore India G.H Raisoni College of Engineering & Management Department of Mca Wagholi Pune India Dr D Y Patil College of Engineering & Innovation Department of Computer Engineering Talegaon Pune India

ISBN: (纸本)9798331543358

Since education is rapidly growing digital, it is more vital than ever to keep a check on students' online activity to make sure they are learning properly. The highly effective Convolutional Neural Network model introduced in this paper is specially developed to recognize student distractions in online learning settings with high precision and minimum processing cost. Our approach is perfect for real-time applications on low-processing-power computers since it uses fewer than 800,000 parameters and achieves an impressive accuracy of 96.13%, in contrast to classic models with many parameters. In order to detect subtle behavioral markers of student engagement and distraction without requiring a lot of computing resources, the proposed method uses a CNN architecture. The model's capacity to generate accurate predictions with low latency was validated through a series of tests conducted in online learning settings with a variety of student behaviors. Our study demonstrates that high-performance engagement monitoring is possible with reduced model complexity, adding a valuable tool for online platforms and educational organizations to enable focused, effective learning experiences. The proposed approach paves the way for future advancements in low-cost, highly accurate student monitoring systems and allows for further research into flexible learning environments. © 2025 IEEE.

关键词： Convolutional neural networks

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Block Chain Technology: Anomaly Detection in Bitcoin Using RFMLPAlgorithm 4

Block Chain Technology: Anomaly Detection in Bitcoin Using R...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Elangovan, V.R. Revathi, S. Nusrat Jabeen, T. Obaid, Ahmed J. Kumar, Rahul SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India City University Faculty Cybersecurity Ajman United Arab Emirates University of Kufa College Al-Ayen University Computer Science and Mathematics Department of Computer Technical Engineering Technical Engineering Thi-Qar Iraq Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Block chain technology has been used in many different fields, including banking, logistics, healthcare, and government. However, billions have been lost to cyber attacks on block chain apps in the last several years. An efficient approach for identifying malicious behavior in block chain networks is urgently required. Anomaly detection is a well-studied issue with a lengthy history of research. Anomalies are, in a nutshell, unusual or improbable occurrences. Theft and other illicit activity in financial networks are often outliers. Participants in the network want to spot anomalies as soon as possible to safeguard the overall safety and security of the system. However, fraud and anomaly detection techniques are constantly developing along with the financial industry. Furthermore, the most secure approach being brought into money is block chain technology. The number of scams, however, increases every year alongside these innovative technology. This is why we suggested a safe Anomaly detection technique that combines Machine Learning with Deep Learning. For the purpose of classifying Anomaly transactions, we used a unique hybrid RFMLP approach. For Bitcoin transaction anomaly detection, the RFMLP combines Random Forest (RF) with multilayer perceptron (MLP). We found that our RFMLP was more accurate than competing algorithms. © 2024 IEEE.

关键词： Bitcoin

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

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computer Systems science & engineering 2023年第1期44卷 859-872页

作者： Fawaz Alassery Ahmed Alzahrani Asif Irshad Khan Kanika Sharma Masood Ahmad Raees Ahmad Khan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Applications Mangalmay Institute of Engineering&TechnologyGreater Noida201310Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

In recent years,it has been observed that the disclosure of information increases the risk of *** restricting the accessibility of information,providing security is ***,there is a demand for time tofill the gap between security and accessibility of *** fact,security tools should be usable for improving the security as well as the accessibility of *** security and accessibility are not directly influenced,some of their factors are indirectly influenced by each *** play an important role in bridging the gap between security and *** this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and *** significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle *** calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare *** study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

关键词： Information security big data big data security life cycle fuzzy AHP

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Digital Forensics and the Law: A computer engineering Perspective 15

Digital Forensics and the Law: A Computer Engineering Perspe...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Gaurav Bhatt, Rahul Pathak, Paresh Kumar, Ashwani Kumar, Anup Pandey, Sakshi Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly243123 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India IIMT University School of COMPUTER Science and Applications Meerut India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India ARKA JAIN University Department of Engineering & IT Jharkhand Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Digital Forensics and the law is an interdisciplinary subject that studies the legal implications of digital evidence in the context of legal guidelines, rules governing investigations, and data-driven tactics for preventing, detecting, and remediating crimes. It explores how the use of virtual forensics techniques impacts the legal manner in crook and civil instances and the expert obligation of those chargeable for using these strategies. The scope of virtual Forensics and the regulation additionally covers the felony implications of the generation-aided discovery system utilized in pursuing investigations into online activity and the capacity to find evidence for which there's no bodily trace or era platform to get admission. Ultimately, the implications of municipal, state, and federal laws governing digital Forensics are also examined. © 2024 IEEE.

关键词： Impacts Municipal Remediating Techniques Utilized

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Mental Health Tracker Using Machine Learning Approaches 4

Mental Health Tracker Using Machine Learning Approaches

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Thotad, Puneeth N. Kallur, Shanta Mundaragi, Laxmi Kadam, Swati H. Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user's mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively. © 2023 IEEE.

关键词： Decision Tree Digital Mental Health K-Nearest Neighbor Logistic Regression Machine Learning Random Forests

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A Deep Review to Find the Most Accurate Method among KNN,RF and MLP for Predicting Alzheimer Disease 4

A Deep Review to Find the Most Accurate Method among KNN,RF ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Subathra, K. Malakar, Antara Suhasini, S. Prakash, Sneha Abed, Sarah Jassim Abdtawfeeq, Tareq Hafdhi Sri Sairam Engineering College Department of Mathematics Chennai44 India The Assam Royal Global University Department of Computer Science & Engineering Guwahati India Cvr College of Engineering Hyderabad India Adi Shankara Institute of Engineering & Technology Department of Computer Applications Kalady India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Debilitating neurodegenerative conditions like Alzheimer's disease (AD) are linked to great pain and eventually death. For the purpose of successful treatment planning and individualized therapy, it is imperative that Alzheimer's disease (AD) and its precursor phases, such as moderate cognitive impairment (MCI), be identified as soon as possible. Single nucleotide polymorphisms, or SNPs, and neuroimaging data, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), have been found to work better together to improve the ability to distinguish between AD/MCI and normal control (NC) participants. But missing data from multimodal sources will always exist, and model learning is complicated by the variety of phenotypic and genotypic biomarkers. Furthermore, most current research uses multistage techniques to forecast illness development and focuses only on classifying brain diseases, ignoring the regression problem. Using the most samples possible inside a k-nearest neighbour, random forest and multi layer perceptron (KNN-RF-MLP) architecture, we provide a unique framework to concurrently solve classification and clinical score regression problems by combining multimodal neuroimaging and genetic data. To impute missing features in partial data, we first apply linear interpolation in our technique. We next employ KNN to extract neuroimaging features from SNPs, MRI, and PET data in order to reduce the variation in phenotypic and genotype data. © 2024 IEEE.

关键词： Positron emission tomography

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