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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Zanke, Pankaj Prasad, Anoop N. Mohan, B.R. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India Sapiens International Corporation Israel East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

True early identification of Parkinson's illness (PD) is absolutely fundamental to stopping the ill health course and planning affected role with access to disease-modifying medications. To do this, significant attention should be paid to the premotor stage of PD. Based on premotor characteristics, a unique deep-learning algorithm is provided to swiftly detect whether or not a person has PD. By identifying and assessing indicators that indicate speech attribute and degree of worsening, a voiced numerical biomarker may supplement the laborious traditional physical test. The project attempts to develop a revolutionary end-to-end deep learning model using speech data for Parkinson's disease identification. The current study employs a mixed deep learning model to evaluate and identify PD. The study's objectives were to identify vocal anomalies in PD patients and create an automatic test technique that can distinguish concerning PD patients' voices and those of fit informs while also grading voice quality. his study investigates the effectiveness of utilizing deep learning architectures for speech data analysis in the context of [insert context]. Specifically, we employ a convolutional neural network (CNN) architecture with recurrent layers (e.g., Long Short-Term Memory, LSTM) to extract hierarchical features from raw speech signals. Our methodology involves preprocessing the raw audio data to extract relevant acoustic features, such as mel-frequency cepstral coefficients (MFCCs) and spectrograms. These features are then fed into the CNN-LSTM model, which learns discriminative representations for various speech tasks, including [mention specific tasks, e.g., speech recognition, emotion recognition, etc.].The category accurateness was evaluated using the Italian PVS speaking quantities and the Lithuanian PD voice dataset. With values of 0.92 showing that the results are medically adequate, the findings demonstrated that the suggested model is highly generalizable. © 2024

关键词： Network architecture

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Analyzing the Feasibility of Bert Model for Toxicity Analysis of Text 6th

Analyzing the Feasibility of Bert Model for Toxicity Analysi...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Chakraverty, Yuvraj Kaintura, Aman Kumar, Bharat Khanna, Ashish Sharma, Moolchand Pareek, Piyush Kumar Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Department of Artificial Intelligence and Machine Learning and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819940707

Online comments can often be toxic, offensive, and harmful to individuals and communities. In recent years, there has been a growing need to automatically identify and mitigate these toxic comments. For this problem, NLP models are often used to identify such toxicity and harshness but each model has its own efficiency and performance limitations. In this paper, we propose the use of the bidirectional encoder representations from transformers (BERT) algorithm for toxicity classification of online comments. BERT is a state-of-the-art natural language processing model developed by Google in 2018 that has shown strong results on a variety of tasks. In this paper, we used the BERT algorithm for toxicity classification and evaluated its performance on a real world dataset and performed comparative analysis with conventional NLP models, logistic regression (TF-IDF) over which BERT showed an improvement of 6.9% in accuracy, 26.1% in f1-score, 21.5% in ROC score;logistic regression (BOW) over which BERT showed an improvement of 9.1% in accuracy, 70.6% in f1-score, 39.8% in ROC score;multinomialNB (BOW) over which BERT showed an improvement of 9.2% in accuracy, 25.9% in f1-score, 10.6% in ROC score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Natural language processing systems

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Mobile-Based Fitness Monitoring System: A Smartphone-Centric Tracker without External Accessories 15

Mobile-Based Fitness Monitoring System: A Smartphone-Centric...

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

作者： Sujatha, E. Devi, P. Jayasri Archana Ilampiray, P. Kalaivani Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani India

ISBN: (纸本)9798331300579

This paper delves into the transformative impact of modern technology on human health, exploring the shift towards utilizing mobile applications as integral tools for health monitoring, control, and management. As society becomes increasingly interconnected and reliant on technology, concerns about the potential health implications arise. This paper investigates the nuanced relationship between technology and health, specifically focusing on the development of mobile applications as a paradigm shift from traditional external gadgets. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. This paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). The employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. The research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. The model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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artificial intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network 1

Artificial Intelligence powered chatbots for the speech disa...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mukherjee, Subhayan Chatterjee, Anusuya Chandrakar, Khushbu Saxena, Sandeep Asansol Engineering College Artificial Intelligence and Machine Learning Asansol India Asansol Engineering College Electronics and Communication Engineering Asansol India Asansol Engineering College Computer Science and Engineering Asansol India Greater Noida Institute of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350356816

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled 'artificial intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network' provides a novel integration of artificial intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities. © 2024 IEEE.

关键词： Assistive technology

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DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection 24

DP-RAE: A Dual-Phase Merging Reversible Adversarial Example ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Jiajie Du, Xia Zhou, Jizhe Pun, Chi-Man Xu, Qizhen Liu, Xiaoyuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Computer Science Engineering Research Center of Machine Learning and Industry Intelligence Sichuan University Chengdu China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798400706868

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements. © 2024 ACM.

关键词： Differential privacy

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Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network 2

Escaping Strategy with Golden Jackal Optimization Based Clus...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Alkhayyat, Ahmad Kumar, Komuravelly Sudheer Hiremath, Murigendrayya M. Das, Abhijeet Madhura, G.K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Department of Civil Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350373110

The Mobile Ad Hoc Network (MANET) is organized with various nodes which communicates wirelessly. Therefore, the communication in MANET suffered from different problems like low stability, high energy consumption and link failure. Additionally, the path failure happens in network because of unrestricted movement of node, that maximized energy usage, delay and minimized nodes lifetime. In this research, the Escaping Strategy with Golden Jackal Optimization (ES-GJO) algorithm is developed for cluster head selection and routing in the MANET network. The appropriate Cluster Head (CH) and routes are chosen using ES-GJO algorithm that boosts network lifetime and predicts link failure with less energy consumption. The ES-GJO algorithm is estimated with metrics of Packet Delivery Ratio (PDR), Delay and Energy consumption for 100 to 500 nodes. The proposed ES-GJO algorithm attained highest PDR of 99.87%, 99.83%, 99.65%, 98.76% and 97.38% for nodes 100, 200, 300, 400 and 500 which is efficient than Eagle Based Density Clustering (EBDC) and Optimal Route Selection Mobile Ad hoc Networks (ORSMAN) algorithms. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Organic Farming Automation to Revolutionize the Agricultural Industry Than Traditional Farming Practices Using IOT and Technological Development

Organic Farming Automation to Revolutionize the Agricultural...

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International Conference on Robotics, Control, Automation and artificial intelligence, RCAAI 2023

作者： Geetha Rani, E. Chalasani, Ramadevi Anusha, D. Dhanalakshmi, M. Vadlamudi, Sandyarani Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Sir C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE-Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Aritficial Intelligence and Machine Learning New Horizon College of Engineeing Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture has remained the spine of human sustenance on earth. Since times unknown, humans have learned and developed methods to produce food and other resources for their livelihood. Agriculture has seen large developments, especially in the last century. Scientific methods have accelerated the process of agriculture to a large extent. These developments have improved productivity by leaps and bounds. Though these developments come with a lot of benefits, they also include several demerits that adversely affect the soil, humans, and livestock. To retain and multiply the benefits contributed by this scientific development and to overcome the demerits and challenges faced in today’s agricultural practices, this paper aims at discussing methodologies that can be used to build an agricultural model that is much simpler and more efficient. The model aims at automation using IoT that could lead to minimized labor efforts by almost refraining human intervention in agricultural practices. machine learning algorithms can be used for weather and soil-based crop detection for better productivity. The downside caused due to the use of synthetic chemicals can be overcome using organic methods. A web portal to facilitate direct producer-to-consumer retail can be deployed to curb the exploitation of farmers by the elimination of middlemen. Hence, the paper aims at developing an agricultural model based on IoT, machine learning, organic farming, and a web portal for direct producer-to-consumer retail. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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Enhancing Collaborative Robots Performance Through Bat Algorithm Based Multi-Objective Optimization 3

Enhancing Collaborative Robots Performance Through Bat Algor...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Sivakumar, R. John Alexis, S. Manickavasagam, A. Rajagopal, Sivakumar Department of Artificial Intelligence and Machine Learning Ahalia School of Engineering and Technology India Department of Mechanical Engineering Ahalia School of Engineering and Technology India Department of Electrical and Electronics Engineering Ahalia School of Engineering and Technology India School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781643685649

Collaborative robots (cobots) are progressively becoming essential in industrial settings, where they operate in conjunction with human workers to execute intricate tasks. Optimising the operational efficiency of cobots entails various obstacles stemming from conflicting aims, including the minimisation of task completion time, reduction of energy consumption, and assurance of safety. This work presents a hybrid optimisation method that integrates the advantages of the Tabu Search algorithm and the Bat Algorithm to tackle these multi-objective difficulties. The Tabu Search is proficient in local search optimisation but frequently becomes ensnared in local optima, whereas the Bat Algorithm, derived from bat echolocation, excels in global search yet may exhibit deficiencies in precision during fine-tuning. The suggested algorithm improves global exploration and local exploitation by hybridising these two strategies, hence ensuring effective job allocation, trajectory planning, and resource management in dynamic collaborative robot environments. The hybrid algorithm is utilised to optimise various objectives inside a simulated collaborative robot environment. Experimental findings indicate substantial enhancements in cobot performance, especially for the equilibrium of work efficiency, safety, and energy consumption, relative to conventional optimisation techniques. This research emphasises the efficacy of integrating bio-inspired algorithms with traditional optimisation methods to address intricate multi-objective challenges in industrial contexts. © 2024 The Authors.

关键词： Collaborative robots

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