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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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Optimizing IoT Workloads for Minimal Time and Energy with UAV Networks Using Deep Q Network 18

Optimizing IoT Workloads for Minimal Time and Energy with UA...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Chauhan, Harshit Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

The rapid growth in the use of IoT devices has highlighted significant challenges due to their limited computational power and battery life, often resulting in long task execution times and potential battery depletion. The problem we address is the inefficiency in task completion and high battery usage in IoT devices. Our key objective is to minimize time delays and energy consumption in IoT workloads. To solve this problem, we propose a partial offloading algorithm for IoT devices, utilizing Unmanned Aerial Vehicles (UAVs) due to their larger battery capacity and greater computational power. UAVs provide flexibility in task management by moving to high-demand areas. Given the battery constraints of UAVs, a network of UAVs is used to share tasks among neighboring UAVs, balancing the load. Additionally, tasks can be offloaded to a ground-based cloud server to prevent overloading the UAV network. Our main contribution is the implementation of a Deep Q Network (DQN) reinforcement learning approach to manage the dynamic nature of task distribution. We achieved a significant reduction in both time delays and energy consumption. Our results displayed approximately a 15-fold reduction in total delay and a 6-fold reduction in total energy consumption, demonstrating the effectiveness of DQNs in this task offloading scenario. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Stress Analysis Prediction for Coma Patient Using Machine Learning 5th

Stress Analysis Prediction for Coma Patient Using Machine Le...

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5th International Conference on Data and information sciences, ICDIS 2023

作者： Alwin Infant, P. Charulatha, J. Sadhana, G. Ragavendra, K. Computer Science and Engineering Panimalar Engineering College Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819969050

Today’s working IT professionals frequently struggle with stress issues. The patient is now more likely to experience stress due to changing lifestyle and workplace cultures. Even while many companies and sectors deliver welfare programs and also make efforts to enhance work environments, the issue remains off from grasp. In this paper, we will utilize various techniques to analyze the stress patterns of working people and identify the variables that exert a massive effect on the system's distress. By utilizing machine learning techniques to forecast models to predict the threats of stressor encountered prognosis through using machine learning, we hope to streamline this procedure. In the working class, mental health illnesses linked to stress are not unusual. Concerns about the same have previously been highlighted by several researchers. In order to do this, information from coma patient answers from working medical experts’ mental health data was taken into consideration. After data extraction and preprocessing, various machine learning approaches were employed to supervise our framework. In this case, the mechanism can apply the machine learning techniques such as decision tree, random forest, KNN, logistic regression, and Naive Bayes. Results of the experiments demonstrated the system’s improved performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Fine-grained Energy-efficient Data Center Traffic Scheduling Mechanism Based on Hierarchical Control

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 742-757页

作者： Niu, Tianyu Li, Honghui Li, Weidong Por, Lip Yee College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Hohhot China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

With the continuous expansion of the data center, its energy consumption is also increasing. Aiming at the problem that the high redundancy of modern data center network causes low energy-consumption utilization, this paper proposes an energy-saving traffic scheduling algorithm (IMV-MLF) based on improved multi-layer-virtual-topology combined with maximum link-utilization path priority, which realizes more fine-grained control of each layer of virtual topology in data center network. Firstly, the relevant shap-ing linear programming mathematical model is established according to the optimization goal of energy conservation, which can minimize the energy consumption of the whole network as far as possible under the premise of meeting all network-flow requirements;Then, according to the optimization goal, the existing SDN based energy saving multi-layer-virtual-topology (EMV-SDN) method is improved. Whether the network devices in other layers are enabled is determined by the overall traffic level rather than the traffic level of a single link. On the basis of the opened level, the newly arrived network flows are preferentially allocated to the path with the highest link utilization, and overload link set is introduced, we can effectively use the available link routes with sufficient residual bandwidth to reduce congestion when the topology changes by marking the links whose link utilization reaches the critical value, and further count and close the links and switches which no network flows pass through. The experimental results show that the performance of IMV-MLF algorithm is better than multi-layer virtual topology energy saving (EMV-SDN) algorithm and the equivalent multipath (ECMP) algorithm in saving network energy. This result is helpful to provide another new idea for energy conservation of data center. © 2023, Journal of Network Intelligence.

关键词： Energy utilization

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Image Recognition technology Supported Environment Monitoring Smart Fan 7

Image Recognition Technology Supported Environment Monitorin...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Yang, Sheng-Yuan Lee, Hsiu-Chia Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user's position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user's home. The fan proves the feasibility of the conceptual architecture. © 2024 IEEE.

关键词： Image recognition

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Integrated feature selection and ensemble learning for heart disease detection: a 2-tier approach with ALAN and ET-ABDF machine learning model

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International Journal of information technology (Singapore) 2024年第7期16卷 4489-4503页

作者： Mandula, Aruna Vijaya Kumar, Baby Shalini Department of Computer Science and Engineering Kalasalingam Academy of Research Education Department of Information Technology Kalasalingam Academy of Research Education

The findings of this investigation give a novel approach to the forecasting of heart disease. For the purpose of determining significant features, it is a 2-tier procedure that uses a combination of the analysis of variance (ANOVA) and Least Absolute Shrinkage and Selection Operator (lasso) regression known as ALAN. In addition to this, it uses the Ensemble Technique with Adaptive Boosted Decision Fusion (ET-ABDF) in order to enhance its accuracy. An improvement in the detection of critical properties for predictive modelling is achieved through the use of the ALAN technique. With an accuracy of 88.0%, precision of 89.81%, recall of 89.80%, and an F1 Score of 89.00%, the ET-ABDF model shows outstanding results in the identification of cardiac disease. The outstanding result of 96.21% for the area under the curve (AUC) shows its strong ability to differentiate across several groups. It has been established through the comparative analysis that the model that has been provided displays better results in comparison to other algorithms. It is possible that in the future, undertaking more research on new techniques for choosing characteristics and expanding studies to encompass a larger spectrum of cardiovascular problems will have the potential to increase the accuracy and practicability of heart disease prediction models in real-world healthcare settings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive Boosted Decision Fusion (ET-ABDF) ALAN ANOVA Lasso regression

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Personalised Emotion Detection from Text Using Machine Learning 6th

Personalised Emotion Detection from Text Using Machine Learn...

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6th EAI International Conference on computer science and engineering on Digital Transformation in Organizations: New Challenges, COMPSE 2022

作者： Bhavya, A.V. Dhanush, R.H. Sangeetha, J. Jose, Arun Cyril Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031347498

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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LEViT- Leaf Disease identification and classification using an enhanced Vision transformers(ViT) model

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Multimedia Tools and Applications 2024年 1-32页

作者： Prashanthi, Bh. Krishna, A. V. Praveen Rao, Ch. Mallikarjuna Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram522502 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

The variety of crops, differences in climate, and the multiplicity of disease symptoms make early identification and evaluation of leaf diseases a challenging task. Although deep-learning methods have been created for this very reason, they often need adjustments to suit various regions and types of crops. This study presents a vision transformer-based deep learning model incorporating Explainable Artificial Intelligence (XAI) features to effectively detect and classify leaf diseases. The model is evaluated using the New Plant Disease Dataset, encompassing data from 14 plants with 38 leaf disease classifications. The primary objective is to enhance accuracy in leaf disease classification through the utilization of vision transformer learning. The proposed model, LEViT (Leaf Disease Identification and Classification using an Enhanced Vision Transformers Model), builds upon the ViT_B_32 model. It incorporates additional layers such as dense layers, dropout, and L2 regularization to improve performance and mitigate overfitting. Specialized preprocessing techniques tailored for transformers are employed to refine data quality. Incorporating Grad-CAM (Gradient-weighted Class Activation Mapping) enhances interpretability, enabling the identification of crucial regions influencing the model’s decisions within images. The model exhibits remarkable efficacy, boasting a training accuracy of 95.22%, validation accuracy of 96.19%, and test accuracy of 92.33%. These results underscore its robust ability to accurately identify leaf diseases across diverse crop types. Comparative analysis with state-of-the-art models in the field demonstrates LEViT’s superior performance, establishing it as a leading solution for leaf disease detection and classification. LEViT’s advanced architecture, coupled with fine-tuning, positions it as a highly promising tool for advancing crop management and yield improvement, thereby significantly contributing to the field of precision agriculture. © The

关键词： Crops

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Transforming Education with Innovative Virtual Learning Environments

Transforming Education with Innovative Virtual Learning Envi...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Shwetha, G.K. Shetty, Pratham S. Baliga, Samarth N. Rathod, Jayant Kumar A. Divya, C. Department of Computer Science and Engineering Karnataka Nitte India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Moodubidire India Kalpataru Institute of Technology Department of Information Science and Engineering Karnataka Tiptur India

ISBN: (纸本)9798350388602

There are several issues with traditional schooling, including poor student participation, rigid scheduling, and restricted accessibility. We require a cutting-edge virtual classroom management system that uses technology to create a dynamic and engaging learning environment in order to solve these problems. The Innovative Virtual Learning Environments is a modern web-based application developed using the MERN stack (MongoDB, ***, ***, ***). It offers a dynamic virtual learning environment, bridging the gap between traditional and remote education by providing features like live video lectures, chat messaging, assignment submissions. By enabling remote attendance, the system removes geographical constraints, enhances accessibility, and offers flexibility and convenience to educators and students. It aims to lower education costs, making high-quality education more affordable and accessible. Utilizing modern web development tools and real-time communication protocols like WebSocket's or WebRTC, the system ensures a interactive and engaging learning experience, promoting teamwork, personalized learning, and real-time communication. The objective is to foster an environment of active learning and continuous interaction, so equipping our users with the critical thinking, creativity, and problem-solving skills necessary to thrive in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Virtual environments

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