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

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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An Innovative Embedded System based Solution for Waste Management 2

An Innovative Embedded System based Solution for Waste Manag...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Chitra, R. Guntanala, Ganesh Bille, Sairaj Moguluri, Yeshwanth Singamshetty, Johnson Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

Waste management is one of the key practical issues faced nowadays. The main objective of this paper is to develop a smart garbage collection system that will help to maintain an effective waste maintenance system. The Swachh Bharat Mission serves as our inspiration for maintaining a clean and eco-friendly environment. As technology is getting smarter, Arduino is used for developing a smart garbage can that will help in keeping the environment clean. The proposed smart trash can management system incorporates an Arduino Uno, ultrasonic sensors, a servo motor, and a battery jumper cable to create an efficient solution. The microcontroller-based design with ultrasonic sensors ensures the proper maintenance of trash cans, preventing pollution and promoting a healthy environment. Neglecting the upkeep of trash cans can result in harmful pollution. The lid of the dustbin opens when someone approaches from a certain distance and stays open until the trash is put inside and the lid is closed. As far as society is concerned, it will promote cleanliness and health. As far as the economy is concerned, it is ensured that the benefits of our work are accessible and affordable to people from all walks of life, regardless of wealth. © 2023 IEEE.

关键词： Waste management

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Wireless Endoscopic Images for Abnormal Alimentary Tract: A Survey 9

Wireless Endoscopic Images for Abnormal Alimentary Tract: A ...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Viswam, Veena K. Ebenezer, Shamila Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350314090

Global statistics show that four out of ten adults are suffering from gastrointestinal diseases of varying severity. Early detection of underlying disorders in the gastrointestinal tract is important in identifying the problems and thereby reducing the mortality rate. Endoscopy is one of the major procedures for examining and diagnosing the human gastrointestinal endoscopic procedure is a regularly used imaging study that is used to clearly detect and characterize tract abnormalities. In this paper, an attempt is made to identify abnormal alimentary tracts. An overview is presented of gastrointestinal tract medical image processing for gastrointestinal disease diagnosis. Diagnostic imaging is an important tool in medical science due to the continuous observations of the expert and uncertainty in medical knowledge. The latest soft computing technologies like machine learning, deep learning, convolutional neural network can be utilized for the early detection of gastrointestinal diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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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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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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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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Segmentation of Brain Tumors using traditional Multiscale bilateral Convolutional Neural Networks 2

Segmentation of Brain Tumors using traditional Multiscale bi...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Asha, P. Ankayarkanni, B. Joshila Grace, L.K. Kaarthik, K.M. Reddy, M. Hemanth Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665491990

Non-automated segmentation of brain tumors from the MRI images is a tedious task, which may sometimes produce inaccurate results. Therefore, the accuracy and reliability of brain tumor segmentation is also key to decision-making, treatment design, and analysis of treatment results. The automated brain tumor segmentation strategy uses manually developed parameters. As with convolutional neural networks, training requires excessive amount of annotated data that is generally challenging to retrieve in the medical field. The prior demand is the design of a task adapted along with robust feature selection and representation. This seems mandatory for effective and efficient segmentation of complicated brain tumors, which in turn is possible with two-channel Convolutional Neural Networks (CNN). Hence, two-channel CNN is adapted for the segmentation as it provided promising results for similar research. The two-channel CNN model contributes much in lessening jitter and over fitting parameters. Conclusively, a two-way cascade design to CNN is introduced, where the base CNN output is processed as an auxiliary source and combined at the final level. When testing the model in the BRATS2013 and BRATS2020 knowledge sets, the CNN integrated package in the 2-channel design was not hidden. Compared to the progressive method described above, the overall performance has been improved and the process quality remains attractive. © 2023 IEEE.

关键词： Decision making

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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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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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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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