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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of Internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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Model Based Quadriceps Activation Strategy in Squat Training with Exhaustion Method Under Different Loads 8

Model Based Quadriceps Activation Strategy in Squat Training...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Xie, Cong Guo, Xin Qie, Botao Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China

ISBN: (纸本)9798350380286

The aim of this study is to explore the muscle activation strategy of quadriceps muscle training in untrained sedentary people under different resistance training to exhaustion to provide control target for body state-based intelligence exercise equipment. Ten daily sedentary men with no training experience performed three sets of heavy load squats (single leg bench squat) in exhaustion mode (4RM) and one set of lower resistance squats (double leg squat with 10 KG garment) in exhaustion mode (18-26RM) to explore which and when the muscle fibers will be recruited as much as more. EMG signals from the dominant side of the vastus medialis), rectus femoris, and vastus lateralis in both squats were recorded, and the maximum RMS of 500 MS length in each movement was normalized to the maximum voluntary isometric contraction. Reliability analysis of the normalized EMG signal for each muscle between the three groups of heavy-load squats was as follows: Alpha (vastus medialis)= 0.885, Alpha (rectus femoris)=0.903, Alpha (vastus lateralis) = 0.925. Paired samples t-test of the three muscles under different resistance:p (vastus medialis) © 2024 IEEE.

关键词： Exercise equipment

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Machine Learning in Healthcare: Prognosis Heart Disease Prediction 4

Machine Learning in Healthcare: Prognosis Heart Disease Pred...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Ramnath, M. Revathi, B. Selva Birunda, S. Usharani, C. Ramana, R. Tyson Masillamani, J. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Thamirabharani Engineernig College Department of Computer Science and Engineering Tirunelveli India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

The heart, being a crucial organ, necessitates meticulous care. Accurate information is essential for identifying heart-related disorders. Precise patient data is vital for hospitals to effectively predict and treat cardiac issues. Utilizing advanced Machine Learning algorithms, such as Logistic Regression, Random Forest, decision trees, K-Nearest Neighbours, Support Vector Machine, Naive Bayes along with statistical models, makes the accurate prediction of heart diseases feasible and accuracy comparison has been made. Finally, a better model has been developed to predict heart disease predictions. Medical practitioners can efficiently detect and address cardiac problems by leveraging these sophisticated algorithms, thereby maximizing the benefits of current medical technology. The application of these algorithms has notably improved patient health and the overall quality of care. Furthermore, hospitals to allocate the resources more precisely perform the predictive analytics more efficiently and enhance patient outcomes. By adopting these technological advancements, medical professionals can stay ahead of potential heart-related issues and deliver better care to their patients. © 2024 IEEE.

关键词： Logistic regression

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Attention based 1-D CNN system for detection of Freezing of gait (FOG) in Parkinsons Patients 15

Attention based 1-D CNN system for detection of Freezing of ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pradeep Kumar, D. Sowmya, B.J. Vincent, Ashvin Pandiyan, Aravindh Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India Bangalore India

ISBN: (纸本)9798350370249

One common clinical symptom seen in Parkinson's disease (PD) patients is freezing of gait (FOG). It manifests as an irregular gait, marked by abrupt, involuntary stopping of movement during gait episodes. FOG entails a major negative effect on PD patients' quality of life. In this work, we analyze the movement data that is collected using the lower-back 3D accelerometer worn by the patients during FOG episodes. By employing machine learning methods to classify this data, we aim to accurately detect FOG trends. In particular, we focus on Deep Learning-based techniques, specifically an attention-based 1D Convolutional Neural Network (1D-CNN), due to their capacity to extract intricate features from unprocessed sensor input. Our results indicate that the proposed model achieves AUC scores of 0.9032 with respect to the TDCSFOG dataset and 0.8682 for the DeFOG dataset, for a combined average AUC of 0.8857. Mean Average Precision (mAP) scores for TDCSFOG and DeFOG reach 0.8388 and 0.779, respectively, for an average of 0.8089, showing the ability of the model to predict FOG occurrences over time. These findings suggest techniques based on Deep Learning enhance FOG detection, allowing clinicians to more effectively tailor treatments and care strategies for Parkinson's patients, thus enhancing their ability to move and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Resume Analyser 15

Smart Resume Analyser

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rane, Milind Zanwar, Harsh Wagh, Kunal Wakode, Suhani Yadav, Avishkar Kulkarni, Atul Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331300579

Every time job recruiters (HR) have to go through a huge pile of resumes submitted by applicants. which becomes a very burdensome task, with Python, NumPy, and Pandas, a resume parser is to be created as part of the project. A structured format will be used to store the data that the parser extracts from resumes, including name, contact information, education, work experience, and abilities. Pandas will be used to store the data in a data frame, and NumPy will be used to manipulate the data. The parser will recognise and extract pertinent information from resumes using methods of natural language processing, improving the accuracy and efficiency of the procedure. By swiftly extracting pertinent information from resumes, the initiative will assist in automating the hiring process and save time for recruiters and HR personnel. © Grenze Scientific Society, 2024.

关键词： Python

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Transfer Learning for Iterative Learning Control Using Gaussian Processes 10

Transfer Learning for Iterative Learning Control Using Gauss...

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10th Indian Control Conference, ICC 2024

作者： Muthiah, Letchumanan Tangirala, Arun K. The Department of Chemical Engineering Department of Data Science and Artificial Intelligence Indian Institute of Technology Madras Tamil Nadu Chennai600036 India

ISBN: (纸本)9798331517212

Iterative Learning Control (ILC) is a technique used to improve the transient tracking performance of a repetitively operated system, such as a batch process. However, the assumption of iteration invariance of the process conditions limits the extension of ILC to similar processes and often demands process modelling and controller design from scratch. data-driven modelling strategies are highly effective but they suffer from large model development cost during training. This is undesirable since operating conditions of industrial batch processes can switch rapidly to meet the requirements of the market leaving little time and resources for model development. In this paper, we present a novel method of transferring optimally tuned ILC across processes using the modern ideas of transfer learning (TL). Specifically, we develop a Gaussian process model using ideas of forward adaptive transfer learning (FAT) to facilitate knowledge transfer between processes. A major advantage of the proposed method is that optimal ILC designed for current process can be transferred to a new process using only limited training data. The proposed method is demonstrated on the Cott-Batch Reactor, showing substantial promise for the proposed TL-based FAT-GP. © 2024 IEEE.

关键词： Federated learning

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Intrusion Detection System using Modified Arithmetic Optimization Algorithm for Large-scale IoT 2

Intrusion Detection System using Modified Arithmetic Optimiz...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Makhadmeh, Sharif Naser Sanjalawe, Yousef Fakhouri, Hussam N. University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman11196 Jordan Department Of Cybersecurity Amman11821 Jordan

ISBN: (纸本)9798350394962

This paper introduces an Intrusion Detection System (IDS) designed to enhance security in expansive IoT networks. The proposed IDS leverages one of thee most popular Machine Learning method with a modified Arithmetic Optimization Algorithm (AOA) to identify the optimal feature set and deal with the binary nature of this optimization problem. This set of features is then employed utilizing the K-Nearest Neighbors Algorithm. Our study utilized a dataset, called BotIoT2018. Notably from the results, the proposed IDS using the modified AOA demonstrated a remarkably precise and resilient detection model tailored for IoT networks. © 2024 IEEE.

关键词： Nearest neighbor search

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artificial intelligence Support for the Prompt Identification and Understanding of the Broader Spectrum of Autism in Children 5

Artificial Intelligence Support for the Prompt Identificatio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Julaiha, A.G. Noorul Hemamalini, S. Preiya, V. Sathya Sathyamoorthy, K. Priyanka, V. Rajalakshmi Institute of Technology Centre for Data Science Department of Computer Science & Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section of population that has actually been forgotten by mainstream economics. Traditional language learning applications often lack specificity for diverse linguistic contexts, focusing on general learning rather than the unique challenges faced by ASD children. The proposed solution leverages Deep Learning (DL) modules to transform partial spoken Tamil words into complete, child-friendly vocabulary. This system goes beyond simple word completion;it incorporates interactive features to engage children with ASD and promote active learning. Preliminary evaluations yielded promising results, suggesting the system's potential in enhancing communication skills among Tamil-speaking children with ASD. This paper research study reviews the limitations of existing language learning tools and the rationale behind employing a DL-based approach. We discuss the specific DL architecture (e.g., Recurrent Neural Networks) envisioned for the system and how it will be trained on a corpus of Tamil speech data. This study also investigate the potential advantages beyond vocabulary learning, such as improved sentence formulation and the development of practical language abilities. Future ambitions include broadening the system's scope to include more languages, refining the DL model through ongoing training with enriched datasets, and conducting extensive user research with varied ASD populations. Validation across demographics is critical to ensuring the system's efficiency and generality. This research has the potential to bridge the language acquisition gap for Tamil-speaking children with ASD, empowering them to develop crucial communication skills and fostering their social inclusion. © 2024 IEEE.

关键词： Recurrent neural networks

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MEDICINE RECOMMENDER SYSTEM BASED ON SEMANTIC AND MULTI-CRITERIA FILTERING

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Interdisciplinary Journal of Information, Knowledge, and Management 2023年 18卷 435-457页

作者： Shambour, Qusai Yousef Al-Zyoud, Mahran Abu-Shareha, Ahmad Adel Abualhaj, Mosleh Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Cybersecurity Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients' health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item prob

关键词： Diagnosis

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Identification of Starch Types by Using Terahertz Time-Domain Spectroscopy Combined with SG-KNN 10

Identification of Starch Types by Using Terahertz Time-Domai...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Heng Wei, Tao Jiang, Yuying Guo, Chunyan Zhou, Wenhui Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China

ISBN: (纸本)9798331520861

The terahertz (THz) spectra of four different types of starch were investigated by terahertz time domain spectroscopy (THz-TDS). Subsequently, standard normal variate (SNV) and savitzky-golay (SG) were used to remove the interference of spectral noise, and then a K-Nearest Neighbor (KNN) method was applied to identify the four types of starch. The results showed that the samples were identified with average accuracy, Kappa, and F1 scores for the test set were 0.9375,0.9158, and 0.9383, respectively. This method was also contrasted with logistic regression (LR). The comparisons demonstrated that SG-KNN surpassed the other methods and indicated that the integration of THz technology with SG-KNN was an effective approach for identifying starch types. © 2024 IEEE.

关键词： Nearest neighbor search

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