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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Doni, A. Ronald Nirmalrani, V. Franklin, Ramya G Poornima, D. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the cutting-edge digital panorama, the amalgamation of multimedia analytics and the Internet of Things (IoT) has ignited a paradigm shift in information usage. This transformation leverages the abundance of visible and audio records, encompassing photos, films, and sound recordings, facilitated via the interconnected IoT community. This convergence gives challenges, which includes information privacy troubles, bandwidth limitations, and the need for superior information processing algorithms. Multimedia analytics, as a subject, revolves throughout the extraction of significant insights from multimedia records. IoT, then again, constitutes a widespread array of interconnected devices and sensors that generate copious portions of visible and audio facts. This mixture permits the actual-time collection, transmission, and evaluation of multimedia information, thereby empowering decision-makers to make extra informed alternatives and benefit profound insights at some point of various domain names. Hence, this paper proposes harnessing the potential of the Internet of Things in Multimedia Analytics (IoT-MA) to enhance the visible and audio facts. The convergence of IoT and multimedia analytics opens up a realm of exceptional possibilities spanning numerous industries and programs. This synergy has a long way-reaching implication in sectors along side healthcare, production, transportation, agriculture, and safety. IoT in multimedia analytics plays a pivotal feature in protection and surveillance, improving public protection, catastrophe response, and danger control. This article sets the stage for a comprehensive exploration of the potential of visible and audio information harnessed through IoT in multimedia analytics. The studies delves into the technologies, applications, and implications of this integration, shedding light on how it's miles reshaping the interactions with the sector and revolutionizing decision-making in an more and more interconnected and informat

关键词： Internet of things

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Safe Characteristic Signature Systems with Different Jurisdiction Using Blockchain in E-Health Records 1

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3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021

作者： Dalali, Shivakumar Pramod, B.K. Kumar, Ranjith Thejas Jain, M.J. Department of Computer Science Engineering Don Bosco Institute of Technology Bengaluru India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

Nowadays users can store the information remotely in the cloud and whenever they want, they can access information at any place with distributed storage system. For the information stored in remote places, information respectability reviewing is used to ensure truthfulness of the information put away in the virtual storage. In distributed virtual storage, for example, the Bank Transaction Records system, the virtual storage may consist of important information. The important information is not to be displayed to the unknown persons while the virtual records are accessed. Accessed documents are encoded and that can understand the sensitive data carefully and neatly. The most effective method to acknowledge information offering to sensitive data are carefully handled in remote places, data reviewing not been systematically examined till today. So, to address this issue, we are bringing the far-off information examine to ensure the idea that acknowledges information directed to sensitive data handled carefully here. In this plan, a tool is brought to clean the information squares comparing to most wanted data of the record and changes these information squares’ marks to essential one for the cleaned document. These marks are carried out to check the completeness of the cleaned document in the span of reviewing. Afterward our scheme uses the record stored in the virtual storage that is accessed and used by needy ones based on the precondition that the important content is secured. The distant information rectitude inspect is ready to be competently executed. Suggested plan is based on personality-based-cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitive data

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Analysis of Performance Metrics for Classification of Punjabi Poetry using Machine Learning Techniques

Analysis of Performance Metrics for Classification of Punjab...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kaushal, Ayush Dutta, Kamlesh National Institute of Technology Hamirpur Computer Science & Engineering Department Hamirpur India

ISBN: (纸本)9798350322309

The poetic text has always been a challenge from the computational linguistics perspective. Literature classification has always been a tedious task in itself owing to so much diversity we have. We have so much information associated with a poem that it can be classified on basis of its writing schemes, on basis of poets, on the grounds of emotions, on basis of an era, etc. In this work, we developed a dataset of 401 poems in total and they were classified into four categories related to different genres. Considering the lexical features of poems, we performed several pre-processing stages to segregate the useful information in form of tokens from poems. These tokens were modeled using Latent Dirichlet Allocation and evaluated using the Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF- IDF) weighting techniques. SVM, Random Forest, Naive Bayes, and Logistic Regression experimented with lexical features in the poem and it was found that SVM proved to be the best- performing algorithm with the highest accuracy of 82.17%. © 2023 IEEE.

关键词： Support vector machines

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RLR: Joint Reinforcement Learning and Attraction Reward for Mobile Charger in Wireless Rechargeable Sensor Networks

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IEEE Internet of Things Journal 2023年第18期10卷 16107-16120页

作者： Shang, Cuijuan Chang, Chih-Yung Liao, Wen-Hwa Roy, Diptendu Sinha Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Tamkang University Department of Computer Science and Information Engineering Taipei25137 Taiwan National Taipei University of Business Institute of Information and Decision Sciences Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

Advances in wireless charging technology give great new opportunities for extending the lifetime of a wireless sensor network (WSN) which is an important infrastructure of IoT. However, the existing greedy algorithms lacked learning from the experiences of energy dissipation trends. Unlike the existing studies, this article proposes a reinforcement learning approach, called reinforcement learning recharging (RLR), for mobile charger to learn the trends of WSNs, including the energy consumption of the sensors, the recharging cost as well as the coverage benefit, aiming to maximize the coverage contribution of the recharged WSN. The proposed RLR mainly consists of three modules, including sensor energy management (SEM), charger location update (CLP), and charger reinforcement learning (CRL) modules. In the SEM module, each sensor manages its energy and calculates its threshold for the recharging request in a distributed manner. The CLP module adopts the quorum system to ensure effective communication between sensors and the mobile charger. Meanwhile, the CRL module employs attraction rewards to reflect the coverage benefit and penalties of waiting time raised due to charger movement and recharging other sensors. As a result, the charger accumulates the learning experiences from the Q -Table such that it is able to execute the appropriate actions of charging or moving in a manner of state management. Performance results show that the proposed RLR outperforms the existing recharging mechanisms in terms of charging waiting time of sensors, the energy usage efficiency of the mobile charger, as well as the coverage contribution of the given sensor network. © 2014 IEEE.

关键词： Reinforcement learning

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IoT and AI-based Intelligent Agriculture Framework for Crop Prediction

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International Journal of Sensors, Wireless Communications and Control 2023年第3期13卷 145-154页

作者： Singh, Pushpa Singh, Murari Kumar Singh, Narendra Chakraverti, Ashish Department of Computer Science & Engineering GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Management GL Bajaj Institute of Technology and Management Greater Noida India

Background: Currently, Artificial Intelligence (AI) and the Internet of Things (IoT) have transformed the field of agriculture with the innovative idea of automation and intelligence. The agriculture field completely relies on the uncertainty parameter of soil, atmosphere, and water. Technological advancement in IoT and AI assist in resolving this uncertainty factor and recommend the best crops to the farmers so that they can also enhance the productivity of the crops and meet the world's large food demand smartly. Objective: In this paper, we have suggested an IoT and AI-based model which trained with 2200 records of the dataset and seven attributes in Python. The model suggests 22 different crops to farmers after collecting samples through different sensor data. We used soil, temperature, humidity, pH, and rainfall sensors. Soil sensors were used to measure the amount of N, P, and K in soil. Methods: Various supervised machine learning algorithms such as KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers have applied to build the proposed model. The model is continuously monitoring the field via various sensor data as a sample data for the prediction of best crops to be grown for farmers. Results: In this research, we investigated the contribution of supervised machine learning classifiers like KNN, Decision Tree, Naïve Bayes and Logistic Regression classifiers. The maximum accuracy has been observed as 99.39% of the Naïve Bayes classifier. Conclusion: In this paper an AI and IoT based model is used to recommend/predict the best crop based on environmental factors. The proposed model will collect the real time sensor data to predict the crops and plants smartly. © 2023 Bentham Science Publishers.

关键词： Agriculture artificial intelligence blockchain crops ICT internet of things machine learning

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Automated Diagnosis of Lung and Colorectal Pathologies Using a Shallow Capsule Network Model 2

Automated Diagnosis of Lung and Colorectal Pathologies Using...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Vishwakarma, Harsh Vani, M. National Institute of Technology Department of Computer Science and Engineering Karnataka Surathkal India

ISBN: (纸本)9798350314168

One of the most hazardous diseases is cancer caused by many biochemical abnormalities. Among all the cancers, lung and colon cancer are the most common and tragic diseases. Effective detection of thoracic and colorectal pathology is vital for timely diagnosis and treatment. In this study, we propose a shallow capsule network model for detecting malignancies in lung and colorectal imaging. Our proposed model is trained and tested on three different datasets: LC25000, IQ-OTHNCCD Lung Cancer Dataset and Chest CT-Scan Dataset with varying train and test data ratios. Despite its shallow architecture, the proposed model achieves high accuracy, with test accuracy metrics of 99.32%, 89.05%, and 99.09% on the respective datasets. We have also shown that the proposed capsule network outperforms traditional deep learning models using less training data. Our findings show that the suggested shallow capsule network model is effective in identifying lung and colorectal disease. © 2023 IEEE.

关键词： Pathology

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Intelligent DDoS Attack Detection for IoT Network Scenario using NS3

Intelligent DDoS Attack Detection for IoT Network Scenario u...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Chaudhari, Shilpa Shashikant Manjula, L. Muttur, Chandana S. Shadaksharaiah, Anushree Maligehalli Preethi, D. Sparshika, T.N. Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350315455

IoT technology and IoT smart devices are being used widely in recent years. As the devices and services are being developed with more sophisticated technologies, their security is a prime factor to rely upon, in order to keep the services up and running properly. Among IoT network cyber attacks, DDoS attacks are prevalent. In order to detect and mitigate these cyber attacks, many machine learning based solutions are being implemented considering the kaggle datasets. this paper focus to detect DDoS attacks in IoT networks by using the proposed hybrid model of Jellyfish Swarm Optimization (JSO) and random forest (RF) algorithms using NS3. The DDoS scenario is designed and simulated in NS3 to generate the IoT DDoS dataset. JSO is bio-inspired algorithm used for feature selection while RF is machine learning used to detect DDoS. The proposed hybrid model improves the accuracy by 2 % compared with RF. Precision, Recall, f1-score statistics are also improved. © 2023 IEEE.

关键词： Bio-inspired model DDos attack IoT devices NS3

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones 6

Quantum Generative Adversarial Network for Autonomous Drone ...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Swarnalatha, Anumula Archana, Nalluri Mukkapati, Naveen Selvakumar, Subramanian Tamir, Eneyachew Manikandaprabu, P. Department of Aeronautical and Aerospace Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh 522213 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9798331522667

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. The RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising;this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results. © 2025 IEEE.

关键词： Generative adversarial networks

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Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Models: A Comparative Analysis

Diagnosis of Polycystic Ovary Syndrome Using Machine Learnin...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Majumdar, Sounak Biswas, Saroj Kr. Purkayastha, Biswajit Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798350325188

Polycystic Ovary Syndrome (PCOS) is a serious health problem affecting a significant percentage of female population worldwide during their reproductive years and adolescence. Besides being one of the leading causes of female infertility, PCOS also carries risks of long-term health issues such as diabetes, high blood pressure, and cardiovascular disease. While PCOS is not curable, proper treatment can alleviate some symptoms. Hence, timely diagnosis is crucial for effective treatment. PCOS diagnosis is challenging due to varying symptoms among affected women. Recently, promising progress in medical diagnosis has been made using machine learning techniques. This paper presents a comparative analysis of the performance of single classifier-based machine learning models and ensemble machine learning models in diagnosing PCOS using benchmark Kaggle PCOS dataset. 10-fold cross-validation has been used to assess the performance of the models. From the experimental results it is observed that among the thirteen different machine learning models considered for this study, the Random Forest classifier has achieved the best performance with an accuracy and F1-score of 90.1% and 83.6% respectively. © 2023 IEEE.

关键词： Accuracy Cross-validation Ensemble Classifiers F1-score Machine Learning PCOS Single Classifiers

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Security Framework for Electronic Health Record in the Cloud Based on Public key Encryption 9

Security Framework for Electronic Health Record in the Cloud...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Aruna, A. Sellam, V. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383201

In the modern world, medical data is managed and stored online so that anybody can access it at any time. For maintaining medical records and sharing with other healthcare providers, the majority of hospitals already use EHR (Electronic Health Records) with cloud storage. Medical data confidentiality, authentication, integrity, and access control are just a few of the security and privacy challenges that have arisen as a result of the growth of healthcare technology and the shift to Cloud computing. In this article, a Medi-locker has been introduced to keep all of the patient's medical records where Both patient and medical practitioners can access healthcare documents at any time, from any location. To maintain the confidentiality and improve the security of the medical data Fully Homomorphic Encryption (FHE), which enables operations to be performed on data that has been encrypted without even decrypting it. The proposed scheme has a high resistance to brute-force attacks when it is compared to other cryptosystem. © 2023 IEEE.

关键词： Data Analytics

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