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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Haripriya, K. Preethi, C. Hammed, S.Shahul Pavalarajan, S. Sharon, S. Akhil Suryakumar, S. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

An IoT-based self-sustained public toilet maintenance system aims to improve hygiene, efficiency, and resource management. It integrates smart sensors, microcontrollers, and cloud connectivity to monitor parameters such as water levels, air quality, and toilet usage in real-time. AI-driven predictive maintenance optimizes cleaning schedules, minimizes downtime, and ensures efficient resource use. Automated water control prevents wastage, while air quality sensors detect harmful gases and activate ventilation. A smart notification system alerts municipal authorities about supply shortages, enabling timely replenishment of water, soap, and tissues. By reducing manual intervention, the system lowers operational costs and improves service quality. Cloud connectivity allows seamless data processing, offering insights for better sanitation management. The integration of IoT and AI ensures proactive maintenance responses. A comparative analysis highlights the novelty of this system in automating and optimizing maintenance. © 2025 IEEE.

关键词： Replenishment (water resources)

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Heart Diseases Prediction with Sound Wave using Support Vector Machine 2

Heart Diseases Prediction with Sound Wave using Support Vect...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Hariharasudhan, S. Komarasamy, Dinesh Aravindh, S. Darshan, S. Saranya, S. Mohana Iyapparaja, M. Kongu Engineering College Department of Computer Science & Engineering Perundurai India VIT School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350349221

Heart illnesses are now an increasing occurrence, and regular human heart testing gets more and more significant. The Phonocardiogram (PCG), a useful diagnostic technique for examining heart sounds, offers insightful data regarding the clinical state of the heart. This research provides an algorithmic method employing PCG sound data to distinguish between abnormal and normal cardiac sounds. Both signal types undergo Cepstrum analysis, and the heart sounds' features are retrieved. Following that, a support vector machine classifier is trained and evaluated using these extracted features, with an accuracy of 95 percentage, generating a method for characterizing heart sound normal or abnormal for PCG signals. © 2024 IEEE.

关键词： Heart

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IoT based Leakage Detection and Emergency Shutdown 6

IoT based Leakage Detection and Emergency Shutdown

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

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Dynamic Sampling-Based Meta-Learning Using Multilingual Acoustic Data for Under-Resourced Speech Recognition

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IEEE Access 2024年 12卷 106070-106083页

作者： Hsieh, I-Ting Wu, Chung-Hsien Zhao, Zhe-Hong National Cheng Kung University Graduate Program of Multimedia Systems and Intelligent Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Under-resourced automatic speech recognition (ASR) has become an active field of research and has experienced significant progress during the past decade. However, the performance of under-resourced ASR trained by existing methods is still far inferior to high-resourced ASR for practical applications. In this paper, speech data from languages that share the most phonemes with the under-resourced language are selected as supplementary resources for meta-training based on the Model-Agnostic Meta-Learning (MAML) strategy. Besides supplementary language selection, this paper proposes a dynamic sampling method instead of the original random sampling method to select support and query sets for each task in MAML to improve meta-training performance. In this study, Taiwanese is selected as the under-resourced language, and the speech corpus of five languages, including Mandarin, English, Japanese, Cantonese, and Thai, are chosen as supplementary training data for acoustic model training. The proposed dynamic sampling approach uses phonemes, pronunciation, and speech recognition models as the basis to determine the proportion of each supplementary language to select helpful utterances for MAML. For evaluation, with the selected utterances from each supplementary language for meta-training, we obtained a Word Error Rate of 20.24% and a Syllable Error Rate of 8.35% for Taiwanese ASR, which were better than the baseline model (26.18% and 13.99%) using only the Taiwanese corpus and other methods. © 2013 IEEE.

关键词： Speech recognition

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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

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computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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Metasurfaces for next-generation wireless communication systems

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National science Review 2023年第8期10卷 17-19页

作者： Younes Ra'di Nikita Nefedkin Petar Popovski Andrea Alù Department of Electrical Engineering and Computer Science Syracuse University Photonics Initiative Advanced Science Research Center City University of New York Department of Electronic Systems Aalborg University Physics Program Graduate Center City University of New York

The pace of development in the world of 5G communication systems has proven to be much more demanding than previous generations, with 5G-Advanced seemingly around the corner [1]. Extensive research is already underway to structure the next generation of wireless systems(i.e. 6G), which may potentially enable an unprecedented level of human–machine interaction [2].

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Unveiling the Invisible: Captioning Videos with Metaphors

Unveiling the Invisible: Captioning Videos with Metaphors

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kalarani, Abisek Rajakumar Bhattacharyya, Pushpak Shekhar, Sumit Department of Computer Science and Engineering IIT Bombay India Adobe Systems India

ISBN: (纸本)9798891761681

Metaphors are a common communication tool used in our day-to-day life. The detection and generation of metaphors in textual form have been studied extensively but metaphors in other forms have been under-explored. Recent studies have shown that Vision-Language (VL) models cannot understand visual metaphors in memes and adverts. As of now, no probing studies have been done that involve complex language phenomena like metaphors with videos. Hence, we introduce a new VL task of describing the metaphors present in the videos in our work. To facilitate this novel task, we construct and release a manually created dataset with 705 videos and 2115 human-written captions, along with a new metric called Average Concept Distance (ACD), to automatically evaluate the creativity of the metaphors generated. We also propose a novel low-resource video metaphor captioning system: GIT-LLaVA, which obtains comparable performance to SoTA video language models on the proposed task. We perform a comprehensive analysis of existing video language models on this task and publish our dataset, models, and benchmark results to enable further research. © 2024 Association for Computational Linguistics.

关键词： Visual languages

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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

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computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

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