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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Latha, J. Murugan, S. Karthick Jayachandran, S. Maheshwari, G. Naveen, G. Navaneethan, S. R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India R. M. D. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tami Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359688

In response to the growing need for advanced artificial intelligence (AI) systems, the study thoroughly examines neuro-inspired deep learning architectures within the domain of cognitive computing. The need for such innovation originates from the constraints of traditional systems, characterized by their inefficiency in duplicating complex cognitive activities. Traditional techniques often need help with complex tasks and more biological neural network flexibility. The proposed system uses neuroscience principles to solve these limitations and create advanced models such as spiking neural networks and hierarchical attention processes, increasing machine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably improves the system's performance across several dimensions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI development. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive computing. © 2024 IEEE.

关键词： Adversarial machine learning

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Sentiment Analysis of Speeches using VADER, Sentiment Intensity Analyzer, and TextBlob

Sentiment Analysis of Speeches using VADER, Sentiment Intens...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ankush Joshi Deepika Joshi Vikash Kumar Anuj Kumar Ritik Kumar Singh Naman Chauhan Computer Science and Application COER University Roorkee India Computer Science and Engineering Amarpali University Haldwani India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

In today’s world the words of a leader are the most valuable entity for his/her followers, and when the leader is a prime minister of one of the largest democratic countries then the value of his/her words is more valuable and important. In this research paper researchers have analyzed the data in the form of sentiments of the two most popular prime ministers of India Dr. Manmohan Singh and Mr. Narendra Modi. By this analysis, researchers can check the impact of their words on the people of India. For analyzing the sentiments researchers have used tools such as TextBlob, VADER, and Sentiment Intensity Analyzer, and for checking the occurrence frequency of words word cloud is used.

关键词： Sentiment analysis Speech analysis Education Tag clouds

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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lassi onk: a system framework to annotate and classify vehicular honk from road traffic

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Environmental Monitoring and Assessment 2024年第10期196卷 1-29页

作者： Maity, Biswajit Alim, Abdul Rama Charan, Popuri Sree Nandi, Subrata Bhattacharjee, Sanghita Computer Application and Science Institute of Engineering and Management Kolkata India CSE National Institute and Technology Durgapur India

Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework $${\varvec{\mathcal {A}}}{\varvec{\mathcal {C}}}$$ lassi $${\varvec{\mathcal {H}}}$$ onk that performs raw vehicular honk sensing, data labeling, and classifies the honk into three major groups, i.e., light-weight vehicles, medium-weight vehicles, and heavy-weight vehicles. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used and proposed an Ensembled Transfer Learning model (EnTL) for vehicle honks classification and performed comparative analysis. Results reveal that EnTL exhibits the best performance compared to pre-trained models and ac

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Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction 38

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary r...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Luo, Haoran Haihong, E. Yang, Yuhao Yao, Tianyu Guo, Yikai Tang, Zichen Zhang, Wentai Peng, Shiyao Wan, Kaiyang Song, Meina Lin, Wei Zhu, Yifan Tuan, Luu Anh School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network 3

Enhancing Facial Expression Recognition with a Directional A...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Masson, Palak Agrawal, Rishi Sharma, Amit Parashar, Amit Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University School of Computer Application Phagwara India Pranveer Singh Institute of Technology Basic Science & Humanities-Chemistry Kanpur India

ISBN: (纸本)9798350384598

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology. © 2024 IEEE.

关键词： Affective computing Attention mechanisms Deep learning Directional Attentive Neural Network Facial expression recognition

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A Study on the Design of Eye and Eyeball Method Based on MTCNN 1

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13th International Conference on Frontier Computing, FC 2023

作者： Hsueh, Cheng-Yu Hung, Jason C. Tzeng, Jian-Wei Huang, Hui-Chun Huang, Chun-Hong Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404348 Taiwan Department of Information Management National Taichung University of Science and Technology Taichung404348 Taiwan Department of Innovation Application and Management Chang Jung Christian University Tainan71101 Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan33306 Taiwan

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Studies on eye tracking have relied on wearable eye trackers and chin-resting eye trackers, but the high cost of equipment and the need to wear devices during experiments can lead to less natural facial movement. This study collected eye-tracking data by using a raw-video camera without adjusting any parameters. Python was used as the primary programming language. Eye tracking was adjusted through calculations of facial distance, and multitask cascaded convolutional networks were used to collect eye-tracking data. The corrected results were visualized, and linear regression was used to determine correction error. The root mean square error was 221.66, and the mean squared error was 260.48. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Eye tracking

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Vibration-induced bias error reduction using loop gain compensation for high-precision fiber optic gyroscopes

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Journal of Systems engineering and Electronics 2025年第1期36卷 224-232页

作者： CHEN Heyu QIN Xuexin XIE Huan KONG Linghai ZHENG Yue School of Instrumentation Science and Opto-Electronics Engineering Beihang UniversityBeijing 100191China Beijing Institute of Computer Technology and Application Beijing 100854China Hunan Shuaiwei Control Technology Co. LTDChangsha 421000China Beijing Institute of Control and Electronic Technology Beijing 100038China

Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

关键词： vibration-induced error fiber optic gyroscope(FOG) loop gain

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Enhancing Security and Privacy in Health Care Using Cyber-physical Systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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Classification and Prediction of Medical Dataset Using Ensemble DeepQ Classifier 4th

Classification and Prediction of Medical Dataset Using Ensem...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Salman, Rahama Gupta, Subodhini Jain, Neelesh Department of Computer Science SAM Global University MP Bhopal India Department of Computer Application School of Information Technology SAM Global University MP Bhopal India Department of Computer Science and Engineering SAM Global University MP Bhopal India

ISBN: (纸本)9789819923212

The clinical dataset consists of a huge amount of disease information and which marks negative effects with respect to features. Feature selection is a major key player removing redundant information. It also increases the decision making result in an effective manner. The important step in feature selections is classification and dimensionality reduction. Medical experts and researchers are using various machine learning techniques to predict and analyze the huge volume of data. The large scale volume of medical dataset is having number attributes and dimensions. Dimensions can affect the analytics and diagnose the results. In this paper we evaluate principle component analysis, support vector machine, factor analysis and ranking methods compared with our proposed CNN-based ensemble feature selection using K-nearest classifier. Our proposed method is followed as standard testing features with parameter optimization. Compared with the existing method our method has 94% of accuracy result by using TensorFlow. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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