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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Shankar, Raghav Singh Kirar, Bhupendra Upadhyay, Pratham Jee Mishra, Shree Govind Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Information Technology Indian Institute of Information Technology Bhopal India

ISBN: (纸本)9798331529833

In the domain of Interactive Systems (IS), machine learning (ML) emerges as a profound, user-friendly, and accurate approach to enhancing emotional intelligence in digital systems. This research work focuses on facial emotion detection, an important but often-neglected aspect of affective computing. Models used for the assessment include Multinomial Naïve Bayes (MNB), Decision Trees (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machines (SVMs), XG Boost (XGB), and Logistic Regression (LR). By employing the FER2013 dataset and MediaPipe's (MP's) facial recognition technology, this research extracts 478 3D facial landmarks to test. The best Acc in emotion detection is found using LR with an accuracy of 65.57% followed by XGB with an accuracy of 61.24%. This study thus will help as a practical application toward making improvements in IS in matters regarding health, marketing, or even security. © 2025 IEEE.

关键词： Adversarial machine learning

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Stellar Classification vis-À-vis Convolutional Neural Network 3

Stellar Classification vis-À-vis Convolutional Neural Netwo...

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3rd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2023

作者： Dutta, Anurag Antony Raj, Albert Stephan Ramamoorthy, A. Harshith, John Soni, Yash Sadh, Unnati Government College of Engineering and Textile Technology Serampore Department of Computer Science Calcutta India Bharathiar University Coimbatore The Tipsglobal Institute Department of Mathematics Tamil Nadu Coimbatore India Anna University Velammal Engineering College Department of Mathematics Tamil Nadu Chennai India Vellore Institute of Technology Department of Computer Science Tamil Nadu Vellore India Shri G. S. Institute of Technology and Science Department of Information Technology Madhya Pradesh Indore India SRM Institute of Science & Technology Delhi-NCR Campus Department of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350338263

As a result of recent advancements in technology, a variety of new computational fields have emerged. Some examples of these fields are machine learning and intelligence, information science, the internet of things, and others. The advancement of humanity will be greatly aided by these fields. The development of Artificial Intelligence led to the creation of a great deal of Neural Networks. Convolutional Neural Networks are one variation of Neural Networks that we are utilizing in this work. These networks are known to perform quite admirably for Image Categorization, which is one of the purposes for which we are utilizing them. The work encompasses Stellar Classification. There are many stellar entities occupying the region known as universal space. Astrophysicists are familiar with a good number of them, but there are still a great many of these types of entities that have not been discovered yet. Because of the great distance that separates our planet from other stellar entities, any attempt to communicate with them through any channel is highly unlikely to be successful. The most information we could possibly acquire is just a guess as to what kind of entity they are. So, if any scientific observatory comes with a nascent search of any distant entity, we could potentially predict which stellar group they belong to. For the purposes of this work, we are only going to focus on two different types of Stella: Stars and Galaxies. For the purpose of training the Convolutional Neural Network, we have used a dataset on Stellar Types with Image Categorization created by the Aryabhata Research institute of Observational sciences (ARIES), which is located in Nainital, India. This dataset was made publicly available. © 2023 IEEE.

关键词： Stars

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Precision Monitoring of Health-Care Using Big Data and Java from Social Networking and Wearable Devices

Precision Monitoring of Health-Care Using Big Data and Java ...

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International Conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Rishabh Goel Satish C J Department of Computer Science and Engineering Vellore Institute of Technology Vellore India

Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big Data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). Data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects.

关键词： Java Wireless sensor networks Social networking (online) Wearable computers Semantics Medical services Ontologies

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An AgroBot: Natural Language Processing Based Chatbot for Farmers

An AgroBot: Natural Language Processing Based Chatbot for Fa...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Anushka Marla Rejath Paul Arupam Kumar Saha Niha Kamal Basha Balasundaram Anandhakrishnan School of Computer Science and Engineering Vellore Institute of Technology Vellore India

In an industry that is enduring a rapid transformation, rowers are gaining access to new technologies that help them improve agricultural yields and resource management. TensorFlow-built chatbots can provide instantaneous assistance to producers. TensorFlow is a machine learning (ML) framework for data automation, model monitoring, and model retraining. It was used to analyse more than 80 million call records from the government portal Kisan Call Canter (KCC) using multiple data preprocessing techniques in conjunction with TensorFlow. Training enabled the chatbot to distinguish between agricultural questions, crop management, soil health, and pest control intentions and entities. This chatbot intends to implement a voice recognition module that will enable users to submit speech-based queries rather than text-based ones. In the future, the chatbot can be trained to comprehend images with high precision and be able to identify crops, provide recommendations on how to increase crop productivity, and promote the need for sustainable farming. The automaton has been evaluated using metrics for precision and error reduction. It's conceivable that TensorFlow-trained chatbots could revolutionize agriculture by providing producers with real-time data and advice on how to increase their yields and revenue.

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Analysis of Symmetric Cryptographical Algorithms in Real Time File Transfer Communication

Analysis of Symmetric Cryptographical Algorithms in Real Tim...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Verma, Rajat Mishra, Sanat Kumar Shukla, Satvik Dhanda, Namrata Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Amity University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350385199

Since the dawn of internet software growth, we have resided in a digital environment fraught with cybersecurity concerns. The sophistication in today's traditional security measures is one of the primary reasons for the increasing number of cyberattacks, thus leaking sensitive data. Encryption, which involves transforming plain text into cipher text, is important for maintaining software security and preventing the unauthorized entry of probable hackers. However, it is evident that the encryption algorithms generally have a high resource requirement in terms of memory, processing time, and electricity. This paper compares ten more common used encryption algorithms: AES, Blowfish, CAST5, Camellia, ChaCha20, DES, 3DES, RC2, SEED, and SM4. There have been evaluations under different conditions: different data types and sizes and requirements for CPU time and encryption/decryption speed. The results of these simulations have pinpointed that each of the algorithms is good enough on its own to be efficient and effective, hence helping the organizations choose the more appropriate technique for their software's security. Therefore, this work importantly contributes to the field of cybersecurity as it adds posing of extensive examination on the most represented encryption algorithms and their performances in different situations. Organizations use the stringent encryption methods to secure their software and data which lessens the odds of cyberattack and data leak. © 2024 IEEE.

关键词： Cybersecurity

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Path Planning for Self-driving Vehicles using Metaverse in 6G era with AI-enabled Networks 18

Path Planning for Self-driving Vehicles using Metaverse in 6...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Choudhary, Rachit Prakash, Rahul Chaudhary, Rajat Budhiraja, Ishan Choudhary, Amit Sir Chhotu Ram Institute of Engineering and Technology Electronics and Communication Uttar Pradesh Meerut India Bennett University School of Computer Science and Engineering Technology Greater Noida India Kth Royal Institute of Technology Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350391725

Self-driving vehicles are the future of technology which is having the potential to reshape mobility by improving the safety, efficiency, and accessibility of transportation system in 6G networks. Critical task like safety is a crucial factor which is included in the planning of motions of the vehicle through an environment where other vehicles and other objects are part of analysis based on feedback control. This paper mainly focuses on decision-making for autonomous driving, specifically on lane change decisions by using deep reinforcement learning (DRL) in metaverse environment. The challenges of the lane change and path planning addresses the importance of considering both longitudinal speed and lateral lane changes in self-driving vehicle scenarios. The study defines the actions for the autonomous agent like either to stay in the current lane otherwise, changing lanes to the left and right. Thus, safety and efficiency are key concerns in the decision-making process based on rewards and penalties defined to encourage safe driving behavior. The paper limits the self-driving vehicle to drive in the three right lanes of the road to ensure safety and adherence to lane regulations. The goal is to drive as fast as possible within the speed limits, avoid collisions, and minimize unnecessary lane changes in self-driving robots environment. This paper implements longitudinal speed control through rule-based methods while focusing on the lane change decision-making process using reinforcement learning algorithms. Finally, the performance evaluation is performed on Matlab and Simulink to compute the path followed by the proposed model. © 2024 IEEE.

关键词： Motion planning

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Diabetes Estimation Through Data Mining Using Optimization, Clustering, and Secure Cloud Storage Strategies

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SN computer science 2024年第6期5卷 781页

作者： Gupta, Shyam S. Pandey, Tushar Kumar Raju, Vadali Pitchi Shrivastava, Rajeev Pandey, Rajeev Nigam, Ankita Roy, Vandana Department of Computer Science and Engineering Amity School of Engineering and Technology (ASET) Amity University College of Community Science Central Agricultural University Indur Institute of Engineering & amp Technology Princeton Institute of Engineering & amp Technology for Women Hyderabad DoCSE UIT-RGPV Department of CSE Princeton Institute of Engineering and Technology for Women Hyderabad Department of EC Gyan Ganga Institute of Technology and Sciences

Asian Indians face a unique risk for type 2 diabetes, with earlier onset and diagnosis at lower BMIs than other populations. Their susceptibility to diabetes-related health risks is higher compared to white populations. India struggles with low public awareness, a shortage of healthcare professionals, and limited treatment accessibility. Innovative strategies using available technological resources could significantly improve diabetes management in India. These challenges, common in developing countries, suggest that insights from India’s experience could benefit global diabetes management. This study aims to develop a hybrid model for predicting type 2 diabetes mellitus (T2-DM) to enhance awareness and preventive strategies. Using the Pima Indian Diabetes Dataset, the model’s effectiveness is validated through various performance metrics. The Fuzzy C-Means (FCM) clustering method is introduced, outperforming traditional models by creating more cohesive clusters with fewer variables. To enhance FCM's effectiveness, the Particle Swarm Optimization (PSO) technique is integrated, preserving FCM's strengths while improving efficiency. The hybrid model's sensitivity is 5.85% higher than K-Means + C4.5, and specificity is 2.79% higher. The proposed model achieves greater accuracy (96.58%) than previous methods. This approach aims to improve diabetes care in India and offers a model adaptable worldwide, addressing a critical global health challenge. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BMI Data mining Diabetic retinopathy FCM PSO T1-DM T2DM

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： K Vishwanath Rachit Reddy Sritama Roy Department of Computer Science Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy Monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena.

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Automated Test Case Generation Using T5 and GPT-3

Automated Test Case Generation Using T5 and GPT-3

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Alok Mathur Shreyaan Pradhan Prasoon Soni Dhruvil Patel Rajeshkannan Regunathan School of Computer Science Engineering Vellore Institute of Technology Vellore India

Test case generation for a given topic can be a challenging and time-consuming task, particularly when the conversation about the topic includes boundary conditions and requirements. This process requires a complete understanding of the system and its scenarios to cover all possible examples, both good and bad. This paper proposes an automated solution for generating test cases using natural language processing techniques. The proposed methodology uses T5 and GPT-3 models to extract the context and topic of the conversation and generate test cases. The model identifies relevant keywords and generates test cases based on them, providing context-based meanings of specific words and defining new terms used in the conversation. The proposed approach eliminates the need for manual test case generation and allows for the testing of more complex systems with a larger number of scenarios. This will reduce the dependency on the expertise of the test designer, resulting in complete coverage of all scenarios. Additionally, this automation will save time and resources, making the process of test case generation more manageable. By utilizing NLP techniques, it will help to ensure that all scenarios are taken into account and that the test cases generated are comprehensive and accurate.

关键词： Communication systems Computational modeling Oral communication Manuals Boundary conditions Natural language processing Task analysis

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