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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Neeraja, A. Begum, Ch. Mounika Reddy, K. Vigneswara Smitha, T.V. Neelima, N. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science Bengaluru India Amrita Vishwa Vidyapeetham Computational Science Lab Amrita School of Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350376517

Exposure to air pollution can significantly affect one's health, which can lead to itchy eyes, breathing issues, and hospitalisation. There are a variety of pollutants such as indoor, biological, and outdoor pollutants. This paper emphasizes estimating the quality of air concerning outdoor pollutants. This model aims to effectively predict the Air Quality Index (AQI) in major populated stations in Bengaluru city. Using data from five monitoring stations - Peenya, Bapuji Nagar, Silk Board, Hombegowda, and BTM Layout- collected between 2021 and 2023, machine learning models are developed to forecast AQI for 2024. The research involved data pre-processing, AQI calculation from key pollutants, and training of models for AQI prediction. Our findings indicate that the Random Forest algorithm outperformed the Decision Tree in both regression and classification tasks, providing more accurate predictions. Additionally, the study included a visual mapping of AQI categories using colour codes, revealing trends in air quality across different regions. This research serves as a foundation for future work, including expanding the model to predict AQI across India and integrating it with real-time data collection hardware to enhance prediction accuracy. © 2024 IEEE.

关键词： Indoor air pollution

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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

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computers, Materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion SDN Wi-Fi

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Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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Storage Solution and Security Transmission in Image Sensing Using Blockchain Technology in Internet of Things

Storage Solution and Security Transmission in Image Sensing ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Gunturu, Vijaya Renu, Nekkanti Dhaliwal, Navdeep Sumalatha, Ippa Anandhi, R.J. Dutta, Gourab School Of Engineering Sr University Department Of Ece Telangana Warangal India Department Of Computer Science And Engineering Visakhapatnam Duvvada India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Brainware University Department Of Computational Sciences West Bengal Kolkata700125 India University Of Kalyani Department Of Computer Science & Engineering West Bengal Kalyani741245 India

ISBN: (纸本)9798350366846

The number of Internet of Things (IoT) devices has grown dramatically with the technology's rapid development. Higher security standards have so been proposed for the administration, transfer, and archiving of vast amounts of IoT data. But security problems like data theft and forgeries are likely to happen while IoT data is being transmitted. Furthermore, the majority of data storage options now in use are centralized, meaning that a centralized server handles both data maintenance and storage. The confidentiality of IoT data would be seriously jeopardized once a hostile assault targets the server. Given the aforementioned security concerns, a secure transmission as well as storage solution for blockchain sensing images in the Internet of Things is put forth. Therefore, to enable effective secure data storage in Internet of Things-related smart computing systems, develop and build a novel blockchain-based artificial intelligence model. We also demonstrated the operation of the system framework. Upon conducting a thorough security study, we have determined that our suggested solution possesses a strong potential to address the majority of security issues that conventional systems encounter. Furthermore, our suggested method can be used for any file-changing wireless Internet of things network that requires the exchange of multimedia data, including traffic data from smart cities, wearable device data, healthcare data, etc. © 2024 IEEE.

关键词： Internet of things

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Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions 2

Enhancing UAV Path Planning Efficiency through Adam-Optimize...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Akshya, J. Neelamegam, G. Sureshkumar, C. Nithya, V. Kadry, Seifedine SRM Institute of Science and Technology Department of Computational Intelligence Chennai603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore641032 India Noroff University College Department of Applied Data Science Kristiansand4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Trajectories

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A Comparative Study of Machine Learning Approaches for Sentiment Classification of Bengali Book Reviews Using Word2Vec

A Comparative Study of Machine Learning Approaches for Senti...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Khalil, Mohammad Ibrahim Alam, Md Tanvir Islam, Akib Jayed Chowdhury, Nazifa Nuha Bhowmik, Subashis Roy Salehin, Sultanus Computer Science and Engineering Port City International University Chattogram Bangladesh Computational Colour and Spectral Imaging Norwegian University of Science and Technology Gjovik Norway University of Dhaka Department of Linguistics Dhaka Bangladesh Electrical and Computer Engineering The University of Alabama Tuscaloosa United States

ISBN: (纸本)9798331535476

This study explores sentiment analysis of Bengali book reviews, addressing a critical research gap in analyzing Bengali text for this domain. Customer reviews and ratings significantly influence online platforms, and automated sentiment analysis offers an efficient method to understand user emotions and opinions. To facilitate this, a dataset of 16,600 Bengali reviews was developed by compiling three datasets and augmenting them with data from online bookstores and Facebook pages. The Word2Vec technique was utilized for feature extraction, employing Continuous Bag of Words (CBOW) and Skip-gram models. Various machine learning classifiers were a pplied to classify sentiment polarity as positive or negative, including Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), AdaBoost, XGBoost, Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), and a Hybrid model. The Skip-gram model paired with the Hybrid classifier achieved t he highest accuracy of 92%. Furthermore, classifiers were e valuated using metrics such as accuracy, recall, precision, Fl-score, and ROC curves. This research advances sentiment analysis for Bengali text, demonstrating effective techniques for predicting sentiment and contributing to broader natural language processing applications for underrepresented languages. © 2024 IEEE.

关键词： Decision trees

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Wireless Network Security Using Load Balanced Mobile Sink Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2135-2149页

作者： Reem Alkanhel Mohamed Abouhawwash S.N.Sangeethaa K.Venkatachalam Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most signiﬁcant resources in the WSN are battery power and *** stra-tegies improve the power factor and secure the WSN *** takes more electricity to forward data in a *** numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less *** possibilities arise due to the cluster head’s limited life *** cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster *** proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of *** addition,the LCLBM was added with an assistant cluster head(ACH)for load *** consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating *** will choose a cluster leader based on the inﬂuence of the fol-lowing parameters on the energy balance of *** to simulatedﬁnd-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

关键词： Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime

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GFlowNet Training by Policy Gradients 41

GFlowNet Training by Policy Gradients

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41st International Conference on Machine Learning, ICML 2024

作者： Niu, Puhua Wu, Shili Fan, Mingzhou Qian, Xiaoning Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Computer Science and Engineering Texas A&M University College StationTX United States Computational Science Intiative Brookhaven National Laboratory UptonNY United States

Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with policy-dependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL). This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL. It is known that the design of backward policies in GFlowNet training affects efficiency. We further develop a coupled training strategy that jointly solves GFlowNet forward policy training and backward policy design. Performance analysis is provided with a theoretical guarantee of our policy-based GFlowNet training. Experiments on both simulated and real-world datasets verify that our policy-based strategies provide advanced RL perspectives for robust gradient estimation to improve GFlowNet performance. Our code is available at: ***/niupuhua1234/GFN-PG. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids 1

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

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computer Systems science & engineering 2023年第5期45卷 1001-1016页

作者： Muhammad Junaid Umer Muhammad Sharif Majed Alhaisoni Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.BOX 84428Riyadh11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every *** BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total *** diagnosis and detection of breast cancer disease with proper treatment may reduce the number of *** gold standard for BC detection is biopsy analysis which needs an expert for correct *** diagnosis of BC is a complex and challenging *** work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology *** evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is *** proposed automatic diagnosis of BC detection and classification mainly involves three ***,a DL model is proposed for feature ***,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best ***,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are *** outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

关键词： Breast cancer deep learning FS IDC ML SVM

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