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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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Secure-M2FBalancer: A Secure Mist to Fog Computing-Based Distributed Load Balancing Framework for Smart City Application 4th

Secure-M2FBalancer: A Secure Mist to Fog Computing-Based Dis...

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4th International Conference on Communication, Device and Networking, ICCDN 2020

作者： Tripathy, Subhranshu Sekhar Barik, Rabindra K. Roy, Diptendu Sinha Department of Computer Science and Engineering National Institute of Technology Meghalaya Shillong India School of Computer Applications KIIT Deemed To Be University Bhubaneswar India

ISBN: (纸本)9789811629105

Nowadays, urban communities are planned to change to a smart city. As per late investigations, the utilization of information from contributors and physical items in numerous urban communities plays a key component in the change toward a smart city. The ‘smart city’ standard is described by omnipresent computing resources for the watching and basic control of such city's structure, medical services, environment, transportation, and utilities. Mist computing is considered as registering model, which performs IoT applications at the edge of the physical devices. To keep up the quality of service (QoS), it is great to utilize context-aware computing as well as cloud computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based on genetic algorithm and reinforcement learning in combination with a load balancing procedure in a secured environment. The proposed Secure-M2FBalancer model directs the traffic in the network unremittingly, gathers the data about each server load, moves the incoming query, and distributes them among available servers equally utilizing dynamic resource allocation strategy. The proposed model comprises four layers, i.e. IoT layer, mist layer, fog layer, and cloud layer. Finally, the results demonstrate that the solutions enhance QoS in the mist-assisted cloud environment concerning maximization of resource utilization and minimizing the makespan with advanced security features. Therefore, Secure-M2FBalancer is an effective method to utilize the resources efficiently by securely ensuring uninterrupted service. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Resource allocation

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Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning 6

Predictive Modeling for Disease Progression in Chronic Condi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Umamaheswari, T.S. Dhaygude, Amol Dattatray Dewangan, Omprakash Krishnan, T. Yerpude, Poonam Swarnkar, Suman Kumar Jayagovind Harigopal Agarwal Agarsen College Department of Computer Applications Chennai India University of Washington Department of Data Science Seattle United States Kalinga University Department of CS & IT Chhattisgarh Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Indian Institute of Technology Department of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350304480

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients. © 2023 IEEE.

关键词： Chronic conditions Clinical applications Clinical variables Data preprocessing Disease progression Feature selection Generalizability Healthcare Longitudinal patient records Machine learning Patient outcomes Predictive modeling

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The Impact of AI-Driven Personalization on Customer Loyalty: A Meta-Analysis of E-commerce Studies 2

The Impact of AI-Driven Personalization on Customer Loyalty:...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Arora, Amishi Kapale, Rahul Sharma, Pratyush Nalavade, Maitreya Saifee, Mubina Ukey, Manda Central Institute of Business Management Research and Development Nagpur India Priyadarshini Lokmanya Tilak of Management Studies and Research Nagpur India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Symbiosis Sspu Pune India Jhulelal Institute of Technology Department of Business Administration Nagpur India Ramdeobaba University Department of Computer Science and Applications Nagpur India

ISBN: (纸本)9798331528713

Artificial intelligence (AI) has changed the way e-commerce works by making it possible to provide unique experiences to a huge number of customers. This review looks at how AI-driven personalization affects customer trust in a number of different e-commerce studies. Putting together the results of several research papers, this study gives a full picture of how well AI works and what it means for increasing customer trust in online stores. The studies were chosen because they had something to do with how AI-driven customizing changes customer behavior and trust measures. The results show that there is a strong link between AI-driven customization and customer trust in online shopping. Personalized suggestions based on AI algorithms were shown in multiple studies to make customers happier, get them to buy from you again, and build long-term ties with them. AI-powered customization of user interfaces and content delivery made the experiences of users better, which increased interest and decreased return rates. The meta-analysis also found a number of factors that affect how well AI-driven personalization works. © 2024 IEEE.

关键词： Marketplaces

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Review of Indoor Positioning System: Technologies and applications

Review of Indoor Positioning System: Technologies and Applic...

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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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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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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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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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