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International Conference on Computing and Networking Technology (ICCNT)

作者： Nadimpalli Madana Kailash Varma Sri Harsh Mattaparty Durgam Nikitha Mohammed Talaha Numan Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telanagana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In contemporary work environments, effective communication, collaborative problem-solving, and positive attitude are pivotal for success. However, conventional hiring approaches often struggle to accurately evaluate these essential competencies. SkillSync presents a groundbreaking solution to this challenge by introducing an innovative assessment platform tailored to measure candidate’s communication skills, problem-solving abilities, and positive demeanor. This project investigates the development of a comprehensive assessment tool designed to analyze an individual’s disposition and adaptability across diverse workplace situations. The tool will leverage a user-friendly interface and a novel algorithmic approach to evaluate an individual’s inherent characteristics. The algorithm will focus on analyzing the relevance and efficacy of candidate responses, with the primary objective of furnishing employers with valuable insights into a candidate’s content writing proficiency and their capacity to approach workplace challenges with a positive outlook

关键词： Employment Focusing Collaboration Writing Position measurement Problem-solving Standards

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Virtual Scribe for Visually Impaired Learners Through Interactive Speech-Based Application 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Sanjeetha, R. Peter, T. John Kallimani, Jagadish S. Sambhram Institute of Technology Bangalore India Department of Computer Science and Engineering Sambhram Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Blind people face various challenges every day, something that people take for granted. One such problem that blind students face is the need for a scribe to write their exam. The onus falls on the blind student to find themselves a scribe which in itself is a difficult task. But even if a blind student has found a scribe, the scribes come with their own set of problems, such as linguistic capability and illegible handwriting. To solve this problem, V-Scribe or virtual scribe is being proposed which is a web application that would completely replace a human scribe. The application uses speech recognition, text-to-speech and a natural language understanding module to understand the requests given by the blind student and give back an appropriate response. The DIET classifier developed by Rasa is trained, obtaining an accuracy of 96.7% for intent recognition and 94.7% for entity recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Sentiment Analysis of machine learning Algorithms: A Transformer-Based Approach

Sentiment Analysis of Machine Learning Algorithms: A Transfo...

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Intelligent Systems and machine learning Conference (ISML)

作者： Nadimpallli Madana Kailash Varma Sudabathula Vijay Sai Kumar Sri Harsh Mattaparty Shifa Ismail Marisetti Harshini Syed Ahmeduddin Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Transformers library. Researcher’s comments from published papers were analyzed using the Transformers library, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Neural Networks consistently outperform other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection.

关键词： Support vector machines Sentiment analysis machine learning algorithms Systematics Neural networks Transformers Libraries Vectors Robustness Splines (mathematics)

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DBNLP: detecting bias in natural language processing system for India-centric languages

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： T. G, Keerthan Kumar Mendke, Saish Parihar, Rohit Mayya, Samarth Venkatesh, Spoorthy Koolagudi, Shashidhar G. Department of Information Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur 572102 India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangalore 575025 India Department of Artificial Intelligence and Machine Learning CSPIT CHARUSAT Gujarat Anand 388421 India

Natural language processing (NLP) is gaining widespread interest and seeing advancements rapidly due to its attractive and exhilarating applications. NLP models are being developed in search engines for real-world scenarios such as language translation, sentiment analysis, chat-bots such as ChatGPT, and auto-completion. These models are trained on a vast corpus of online data, exposing them to harmful biases and stereotypes towards various communities. The models learn these biases, making harmful and undesirable predictions about particular genders, religions, races, and professions. Biases in NLP systems can perpetuate societal biases and discrimination, leading to unfair and unequal treatment of individuals or groups. It is crucial to identify these biases, which will help mitigate them. Most of the literary works in this area have been primarily Western-centric, focusing on the English language, making it tough to use them for Indian models and languages. In this work, we propose a model called Detecting Bias in Natural Language Processing System for India-Centric Languages (DBNLP), which aims to identify the biases relevant to the Indian context present in the text-based language models, particularly for the English and Hindi languages. The DBNLP presents three techniques for bias identification based on (1) a Context Association Test (CAT), (2) a template-based perturbation technique for various co-domain associations, and (3) a co-occurrence count-based corpus analysis technique. Further, this work showcases how India-centric models such as IndicBERT, MuRIL, and datasets such as IndicCorp are biased toward various demographic categories. Detecting bias in natural language processing systems for India-centric languages is essential to creating fair, diverse, and inclusive models that benefit society. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Diverse and Inclusive models Harmful biases and stereotypes India-centric models Indian context Natural language processing

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Automatic Modulation Classification for CR Using Deep learning

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SN Computer Science 2024年第8期5卷 1061页

作者： Solanki, Surendra Brahma, Banalaxmi Singh, Yadvendra Pratap Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Dehmi Kalan Rajasthan Jaipur 303007 India Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Automatic Modulation Classification holds significant importance in the realm of wireless communication systems. The proliferation of modulation schemes, coupled with the escalating complexity of channel environments, necessitates a heightened accuracy in discerning modulated signals. Traditional methods struggle to effectively classify this array of modulation schemes. In response, this paper advocates the employment of deep learning techniques for precise modulation classification. A deep learning architecture is introduced, comprising a convolutional neural network, and a long short-term memory neural network leveraging the power of transfer learning. The convolutional neural network adeptly extracts spatial features from the received signal, while a bidirectional long short-term memory neural network is engineered to capture temporal features. Transfer learning is employed for efficient parameter sharing. Empirical evaluations were conducted using the RadioML2016.10b dataset, affirming the efficacy of the proposed mechanism. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolutional neural network Long short term memory Modulation modes Transfer learning

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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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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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Breast Cancer Detection Using Ultrasound Images Based and Convolutional Neural Network

Breast Cancer Detection Using Ultrasound Images Based and Co...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Meher Langote Kunal Hiwase Radha Wande Abhay Tale Harsh Kalekar Swapnil Gundewar Faculty of Engineering and Technology Sawangi Artificial Intelligence and Data Science Maharashtra India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Sawangi Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Breast cancer forms one of the most common types of cancer among women worldwide; therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized.

关键词： Training Surveys Visualization Ultrasonic imaging Accuracy Breast cancer Sensors Convolutional neural networks Reliability Testing

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Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor Data and machine learning Integration 5

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

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

作者： Chandiraprakash, N. Chinnasamy, A. Ashok, M. Malla Reddy College of Engineering Department of Artificial Intelligence and Machine Learning Hyderabad India School of Computing Department of Data Science and Business Systems SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (纸本)9798331518097

This research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. The aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. This study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. This study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. This technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. These results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations. © 2024 IEEE.

关键词： Smart agriculture

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