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2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Roy, George Tonmoy Biswas, Dipannita Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (纸本)9798331508227

Sentiment analysis is one of the important aspects of discovering user opinions and enhancing the quality of products like mobile applications. This study assesses the effectiveness of six prominent Natural Language Processing (NLP) models—BERT, RoBERTa, XLNet, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying sentiments from a large dataset of Google Play Store reviews. We collected 73,483 reviews from 45 apps, preprocessed the data, and employed two labeling methods: a pre-trained DistilBERT model and K-means clustering. Our results compare transformer-based models, BERT and XLNet, with traditional recurrent models for sentiment analysis of mobile application reviews. The results show that transformer-based models achieve higher accuracy, precision, recall, and F1 scores with DistilBERT pre-trained model labeling. However, no single model strikes a balance between recall and precision using K-means clustering labels. These findings suggest that the choice of labeling method, in addition to the use of advanced transformer models and embeddings, significantly affects model performance. ©2024 IEEE.

关键词： Long short-term memory

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Escherichia Coli Bacterial Identification using Deep Learning Approach 1

Escherichia Coli Bacterial Identification using Deep Learnin...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Tasneem, Nelufar Sunanda Ramesh Babu, D.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

Urinary Tract Infections (UTIs), predominantly caused by Uro-Pathogenic Escherichia Coli (UPEC), account for 80% of cases, posing a significant health challenge. Traditional methods for bacterial identification, such as culturing, are both time-consuming and costly. In this study, a Deep Learning (DL) approach using a Convolutional Neural Network (CNN) model developed with TensorFlow's Keras API is presented to quickly and accurately identify E. coli and non-E. coli bacteria colonies from a custom dataset of 978 images collected using mobile devices. The model, comprising three convolutional layers (32, 64, and 128 filters), was trained for 30 epochs, achieving 91% accuracy. Max pooling, dropout layers, L 2regularization and early stopping was implemented to prevent overfitting. data augmentation was also applied to enhance model generalization. The findings demonstrate that this cost-effective, real-time deep learning method provides an efficient alternative to conventional identification techniques, offering a promising tool for rapid diagnostics in clinical environments. © 2024 IEEE.

关键词： bacterial colonies convolutional neural network deep learning escherichia coli urinary tract infection

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NIDR: Network Interference Detection and Rectification 4

NIDR: Network Interference Detection and Rectification

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Rose Mary, K.A. Joy, Celestine Devis, Denin Fransisco, Irene Devassy, Deepa George, Livya Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307757

In the contemporary digital landscape, ensuring the continuous functionality of computer networks is vital for business and critical infrastructure success. This research project utilizes cutting-edge technologies like machine learning and anomaly detection to monitor network traffic and system logs. Through in-depth analysis of traffic patterns, identification of attack signatures, and prompt anomaly detection, the system provides accurate and timely identification of network attacks. Key components include real-time monitoring, comprehensive attack signature databases, and intelligent alerting for swift response, aiming to empower small-scale organizations in safeguarding their data and effectively responding to evolving cyber threats. © 2024 IEEE.

关键词： Anomaly detection

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Sparkle - Mobile Application for Real-Time Activity Analysis and Mental Wellness Insights

Sparkle - Mobile Application for Real-Time Activity Analysis...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kavitha, D. Desigan, M. Subanandham, E.R. Kumar, S. Naren Pranaavv, P. Madhan, H. Sri Sairam Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

The normal human brain always searches for easy ways to complete a task. It will be good to procrastinate for one or more activities but for other tasks it won't be suitable for us. The purpose of this project is to help people who procrastinate valuable work for instant gratification and they will be guided to do their proper work within a given time through a mobile application and will help the people to realize that they are wasting their time on applications which are meant only for entertainment. It will let them know the changes in their mental capacity or mental capacity due to their addiction to instant gratification. So the people can know their current mental status and they can improvise themselves to overcome the addiction. They will be given ideas about being productive and consistent to achieve long term goals. The main idea behind this project is to let people realize that achievements in reality are totally different from imagination. © 2024 IEEE.

关键词： Amines

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Superior stability of Li5Mg@Cu anodes for lithium metal batteries:Investigating the suppression effects of magnesium on lithium dendrite growth

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材料科学技术（英文版） 2025年第8期211卷 288-302页

作者： Ruijun Yao Zhuoyu Li Longke Bao Rui Deng Kai Zheng Yiming Hu Jiahui Li Hao Zhang Shaobo Tu Rongpei Shi Junwei Wu Changming Li Xingjun Liu College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Process Engineering Graduate School of EngineeringNagoya UniversityFuro-choChikusa-kuNagoya464-8603Japan School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China 21C Innovation Laboratory(21C LAB) Contemporary Amperex Technology Ltd.Ningde 352100China Institute for Materials Science and Devices Suzhou University of Science and TechnologySuzhou 215011China

Li metal is widely recognized as the desired anode for next-generation energy storage,Li metal batteries,due to its highest theoretical capacity and lowest ***,it suffers from unstable elec-trochemical behaviors like dendrite growth and side reactions in practical ***,we report a highly stable anode with collector,Li5Mg@Cu,realized by the melting-rolling *** Li5Mg@Cu anode delivers ultrahigh cycle stability for 2000 and 1000 h at the current densities of 1 and 2 mA cm-2,respectively in symmetric ***,the Li5Mg@Cu|LFP cell exhibits a high-capacity retention of 91.8％for 1000 cycles and 78.8％for 2000 cycles at 1 ***,we investigate the suppression effects of Mg on the dendrite growth by studying the performance of LixMg@Cu electrodes with different Mg contents(2.0-16.7 at％).The exchange current density,surface energy,Li+diffusion coefficient,and chem-ical stability of LixMg@Cu concretely reveal this improving suppression effect when Mg content becomes *** addition,a Mg-rich phase with"hollow brick"morphology forming in the high Mg content LixMg@Cu guides the uniform deposition of *** study reveals the suppression effects of Mg on Li dendrites growth and offers a perspective for finding the optimal component of Li-Mg alloys.

关键词： Lithium dendrite Lithium metal anode Lithium-magnesium alloy Cycle performance Suppression effect Stability

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor 2

IoT-Enabled Covert Surveillance Camera Identification throug...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ruksana, Shaik Imran, Shaik Farheen, Shaik Fatima Kireeti, Krovi Mohan Sailaja, K.L. VR Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India VR Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350371314

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adeptly spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all. © 2024 IEEE.

关键词： Cost effectiveness

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Mind controlled Movements: Directing Action with left and right thoughts cooperating with EEG 2

Mind controlled Movements: Directing Action with left and ri...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sajitha, A.K. Srithilak, R. Karthic, Vikas K. Rathi, G. Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350383867

This study demonstrates the potential of Brain-computer Interface (BCI) technology to enable individuals to control devices simply through thought, specifically focusing on actions such as moving left and right. Leveraging machine learning techniques, particularly linear regression analysis, on real-time datasets, the research endeavors to develop an application tailored for individuals with paralysis. The process involves the systematic collection of data through the placement of electrodes to capture brain signals associated with the intention to perform left and right movements. These collected datasets are then processed and prepared to train a machine learning model. Through iterative training, the model learns to accurately interpret the brain signals associated with the intended actions of moving left or right. The results of the study underscore the efficacy and precision of the real-time datasets collected, as demonstrated by their successful implementation within the machine learning model. By successfully splitting and discerning between the signals corresponding to left and right movements, the model showcases promising potential for practical application in assisting individuals with paralysis. Overall, this research represents a significant step towards harnessing the capabilities of BCI technology combined with machine learning to enhance the quality of life for individuals facing motor impairments, offering new avenues for intuitive and efficient control of assistive devices through the power of thought. © 2024 IEEE.

关键词： Fast Fourier transforms

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Self-annotated Labelling and Training data for Traffic Video Object Detection Using Machine Learning Techniques 7th

Self-annotated Labelling and Training Data for Traffic Video...

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7th IFIP TC 12 International Conference on Computational Intelligence in data science, ICCIDS 2024

作者： Rahul Chiranjeevi, V. Swamy, Maanesh M. Krishna Prasath, M.K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9783031699818

Classical machine learning algorithms are susceptible to objective elements like video quality and the weather, which results in inferior detection results in an erroneous identification of Unmanned Aerial Vehicle (UAV) action photos. Deep learning is suggested for identifying cars in UAV video. Thanks to the technology offered by road management systems, real-time visual information is now accessible in thousands of locations on road networks. The first step in identifying or preventing accidents is locating the vehicles on the path of travel. Convolutional neural networks have made substantial advancements in the field of object detection. Enhancing conventional computer vision techniques. However, there are drawbacks because the pre-trained models that are currently available only provide a low detection rate, especially for small objects. The major drawback is that, in order to retrain the automobile maker, they must manually identify the vehicles that appear in the photographs captured by each IP camera on the road infrastructure. This task will be impossible to complete even if we install hundreds of cameras throughout the extensive road network. The technique utilized in this study uses CCTV footage with a comparable range of images with the aim of identifying cars in an image. The spatial brightness of the original sample is first translated using the HSV (Hue, Saturation, Value) method to maximize sample diversity and adaptability to different lighting conditions. The performance of the basic SSD is enhanced by incorporating focus loss because of feature extraction. Following a trained neural network model’s examination of the UAV footage, the effectiveness of drone identification is assessed. The results show that the proposed method have a car detection rate of 96.49%. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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Intelligent Tutoring Systems using Long Short-Term Memory Networks and Bayesian Knowledge Tracing 5

Intelligent Tutoring Systems using Long Short-Term Memory Ne...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Subha, R. Gayathri, N. Sasireka, S. Sathiyabanu, R. Santhiyaa, B. Varshini, B. Sri Eshwar College of Engineering Department Of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350395235

Educational systems often deliver uniform coursework and exams to all students, irrespective of their prior knowledge, interests, or learning ability. This absence of personalization can lead to reduced engagement levels and diminished learning outcomes. The adoption Intelligent Tutoring System (ITS) is driven by its recognition that each learner is unique, with distinct strengths, weaknesses, and learning styles. Traditional classrooms typically cannot accommodate these variations effectively, leading to a significant achievement gap among students. Moreover, ITS excels at promoting self-directed learning by providing instant feedback, hints, and tailored assessments, which motivates the learner to take initiative in their learning process. This shift towards self-directed learning not only fosters a sense of autonomy and responsibility but also equips learners with valuable skills such as problem-solving, critical thinking, and resourcefulness. This study proposes an ITS which uses Long Short-Term Memory (LSTM) networks and Bayesian Knowledge Tracing (BKT). Through the seamless integration of LSTM across key modules, the Intelligent Tutoring System offers content-level recommendations, dynamic assessments, and real-time support for learner doubts and questions. The study aims to enhance learner engagement, satisfaction, motivation, and knowledge retention by personalizing learning content to individual needs and preferences. © 2024 IEEE.

关键词： Long short-term memory

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Early Identification of Disease in Indian Gooseberry using Machine Learning and Deep Learning Techniques 1

Early Identification of Disease in Indian Gooseberry using M...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Priyanka, G. Mahalakshmi, A. Padmavathy, C. Nandhini, R. Sathya, S. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518943

The Gooseberry cultivation is crucial for both commercial and domestic purposes, but its productivity is threatened by various diseases. Some of the commonly occurring disease are soft rot and rust disease. Traditional methods for disease detection and classification consume a lot of time and often lack accuracy. In this study, we design a novel approach leveraging machine learning as well as deep learning techniques, incorporating Support Vector Machine, Convolutional Neural Networks and ResNet18 for the prediction of gooseberry fruit diseases. Furthermore, we integrate image segmentation using the K Means clustering technique to precisely identify regions of interest within the images. Our methodology involves preprocessing the images, applying image segmentation to the preprocessed images, training the deep learning models on annotated datasets, and validating the models using testing datasets. We specifically employ K Means clustering for image segmentation to isolate disease affected areas. Experimental results show the efficacy of the proposed approach in accurately predicting gooseberry fruit diseases and segmenting the affected regions within the images. It was observed that ResNet18 was found to predict the disease in the Gooseberry fruit precisely with an accuracy of 96.1%. © 2024 IEEE.

关键词： Fruits

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