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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sureka, N. Vasanthakumar, G.U. Swathypriyadharsini, P. Alabdeli, Haider Prashanthi, Bhupathi Agni College of Technology Department of Biomedical Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India The Islamic University Najaf Iraq Griet Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350382952

The Cognitive Radio (CR) has been established as an emerging technique which enables underused or unused spectrum for dynamically enhance the spectral effectively. To enhance the CR performance which is extensively significant to reconfigure or adapt the model parameters. The Multi-Objective Improved Salp Swarm Algorithm (MOISSA) is proposed for spectrum sensing in CR networks. The objective functions such as throughput, energy efficiency and interference are formulated to define the spectrum sensing performance in CR networks. In this paper, the parametric alteration of CR is accomplished through SLSSA which exploits the areas near to individual location to attain its global optimum. The SSA is used to avoid local optima and define the suitable optimal solution evaluation in whole optimization process. In traditional SSA, the Self Learning (SL) rule is applied to provides every learner opportunity to enhance its individual experience through search. The performance of MOISSA is estimated by various metrics like delay, Packet Delivery Ratio (PDR), energy consumption and network lifetime are used with 20, 40, 60, 80 and 100 nodes. The MOISSA attains delay of 13.4s, PDR of 59.7%, 8.55J of energy consumption and 27.5 of network lifetime for 100 nodes which is better when compared to existing method such as Improved Whale Optimization Algorithm with Convolutional Neural Network (IWOA-CNN). © 2024 IEEE.

关键词： Cognitive radio

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Explainable Emotion Recognition in Social Networks with Transformers

Explainable Emotion Recognition in Social Networks with Tran...

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International Conference on computer and Information Science (ACIS)

作者： Isidoros Perikos Chara Pylarinou Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press “Diophantus” Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences.

关键词： Human computer interaction Emotion recognition Analytical models Sentiment analysis Social networking (online) Computational modeling Predictive models Transformers Data models Artificial intelligence

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. The accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. This paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text Processing (SMTP) as a loss function. The experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. This algorithm stands out as a more efficient approach for melanoma stage classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

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computer Systems Science & engineering 2023年第9期46卷 3285-3301页

作者： A.N.Arun P.Maheswaravenkatesh T.Jayasankar Department of Computer Science&Engineering Sri Venkateswara Institute of Science and TechnologyTiruvallurTamilnaduIndia Department of Electronics and Communication Engineering Anna UniversityTrichyTamilnaduIndia

The human face forms a canvas wherein various non-verbal expressions are *** expressional cues and verbal communication represent the accurate perception of the actual *** many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person *** when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro *** micro expressions cannot be masked and reflect the actual emotional state of a person under *** expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize *** necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on *** study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to *** research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less *** of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the *** proposed swarm-based technique handles this in the research to precisely alig

关键词： Facial micro expression recognition deep learning CNN artificial bee colony

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Face Mask Detection Using Support Vector Machine

Face Mask Detection Using Support Vector Machine

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2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Divya Priya, D. Subbulakshmi, R. Purushotham, P. Kiran, Ajmeera Arulananth, T.S. Kavya, S.P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore India MLR Institute of Technology Department of Electroncis and Communication Engineering Hyderabad India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350348217

According to a report published by the World Health Organization (WHO), the only two primary ways in which the COVID-19 virus can be spread are through breathing contaminated air and through direct physical contact. Wearing a mask is one of the bare minimum preventative measures that can be taken against the transmission of certain viral infections, including COVID-19. Therefore, it is much more important to wear a mask properly in very crowded areas such as public parks, supermarkets, and shopping malls. This is because the risk of spreading an infectious disease is significantly higher in these settings. Face Mask Detection is a technique that we developed so that we could avoid problems like these. It can determine whether or not a person is wearing a mask on their face. There have only been a handful of scant studies done on face mask detection. © 2023 IEEE.

关键词： SARS

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Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network

Enhancing Patient Well-Being in Healthcare Through the Integ...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Divakaran, Prakash Lingareddy, Nagulapalli Deepika, Sheri School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India Himalayan University Itanagar India Vignan Institute of Technology and Science Department of CSE Deshmukhi India Sreyas Institute of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798350376470

This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. © 2024 IEEE.

关键词： Support vector machines

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IoT-based Patient Monitoring System through Online Cloud and ECG Sensor

IoT-based Patient Monitoring System through Online Cloud and...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Amir-Ul-Haque Bhuiyan, T.M. Ahmed, Shakhir Salahin, Md Pollab, Md Jui, Jannatun Nyeem Ahmmed, Md Tanvir Rahman, Anichur Wadud, Md. Anwar Hussen Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Bangladesh Bangladesh University of Business and Technology Department of Computer Science and Engineering Bangladesh Constituent Institute of University of Dhaka Bangladesh

ISBN: (纸本)9798350316001

The Internet of Things (IoT) is revolutionizing healthcare by enabling remote patient monitoring. An IoT-based patient monitoring system is the main focus of this study that uses an electrocardiogram (ECG) sensor, a wire thermometer sensor, and a DHT11 sensor. The objective of the system is to continuously monitor the ECG signals of patients, monitor patients' body temperature and patients' room temperature and humidity, and transmit them wirelessly to a cloud-based server for real-time analysis. The proposed system comprises an ECG sensor, a one-wire thermometer sensor, a DHT11 sensor, a microcontroller, and a wireless communication module. The ECG sensor captures the electrical signals generated by the heart and converts them into digital signals. The DS18B20 sensor measures patients' body temperature, and the DHT11 sensor monitors room temperature and humidity. The microcontroller processes the digital signals and transmits them to the cloud-based server through the wireless communication module. The smartphone Apps take patients' records from the cloud, analyze the ECG signals, and generate necessary suggestions if any abnormalities are detected. © 2023 IEEE.

关键词： Electrocardiograms

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FRCD: Future Ready Career-Duck a Machine Learning-Driven Career Guidance for Students

FRCD: Future Ready Career-Duck a Machine Learning-Driven Car...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Paritosh, Priyanshu Yadav, Vedant Gupta, Dev Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Mechatronics and Automation Engineering Bhagalpur India Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798331540821

In today's society, finding a suitable career is crucial. High school students frequently need to fully understand the notion of labor due to a lack of experience and practical knowledge in various disciplines. This renders students particularly vulnerable to external influences while deciding on a career path. As a result, students must carefully consider high-paying careers to ensure that they align with their interests and skills. Otherwise, students may end up in jobs they dislike or are not suited to. In response to this issue, we intend to develop a machine-learning framework capable of predicting career pathways. Students will receive career recommendation from our model based on their performance and potential. Students can confidently follow their chosen fields if they do well academically;if not, they can choose to change to other fields that better suit them. There are challenges in determining the metrics and improving prediction accuracy to quantify students' career paths and performance in teaching-learning. Best on the test results, the proposed model has a higher prediction accuracy. Also, this method is both reliable and portable, and it may be used to predict career paths in real time. © 2024 IEEE.

关键词： Students

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. Tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

The use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. The study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. This approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from GitHub to demonstrate the methodology. The sample is selected to reduce the complexity and to increase the performance. The dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. This improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP International Federation for Information Processing 2024

关键词： Snow

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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