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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Singh, Kamakhya Narain Samui, Anupam Mukul, Manas Misra, Chinmaya Goswami, Biswadev School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Electronics Engineering Kiit Deemed to Be University Bhubaneswar India Dr. B C Roy Engineering College Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350370188

This paper investigates the User Interface (UI) and User Experience (UX) design of various online learning/educational platforms to improve student's satisfaction. In order to find out the importance of UI/UX design, an empirical web based survey was conducted about user experience in terms of UI design of websites including audio, video clarity, screen sharing, messaging chat, number of maximum participants, network adaptability, course, name, age, cost and demographic location. Udemy is their most preferred online learning platforms followed by Coursera. In this study, we give a number of user studies that may be used to evaluate UI and UX and examine usability as well as utility. Additionally, applying ML techniques on curated dataset to recommend best UI design of websites for learners. The experimental result depicts XGB outperforms better than other classifiers for Udemy. © 2024 IEEE.

关键词： User interfaces

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Cervical Cancer Diagnostics Using Machine Learning Algorithms 5

Cervical Cancer Diagnostics Using Machine Learning Algorithm...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Chikaraddi, Ashok K. Kanakaraddi, Suvarna Kalwad, Priyadarshini Kadole, Atharv Budni, Shashikala Computer Applications K L E Technological University Karnataka Hubli India School of Computer Science and Engineering K L E Technological University Karnataka Hubli India

ISBN: (纸本)9798350361155

A rising number of people and businesses are using Deep Learning (DL) and Machine Learning (ML) to analyze vast volumes of data and provide insights that can be put to use. In medical practice, it is becoming more and more usual to use machine learning methods to predict major illnesses such as cancer, kidney failure, and heart attacks in their early stages. One of the most common illnesses affecting for women is cervical cancer, which may be prevented with an early diagnosis. As a result, this work considers suitable ML algorithms to detect cervical cancer. Predictive Model Selection (PMS), data pre-processing, are performed for the chosen dataset. A variety of traditional machine learning techniques, such as Decision Trees (DT), Logistic Regression (LR), Support Vector Machines (SVM), adaptive boosting, gradient boosting, random forest, K-Nearest Neighbors algorithm (KNN), and XG-Boost, are tested and reported in the PMS section. Regarding the prognosis of cervical cancer. In this work trained the dataset on three models, VGG16, Inception-v3, ResNet-50, and also developed and analyzed the performance of Ensemble model. © 2024 IEEE.

关键词： Decision trees

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Role of internet of things in protecting different wearable gadgets and materials

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Materials Today: Proceedings 2022年 56卷 3387-3393页

作者： Prasant, P. Bhardwaj, Shashank Gupta, Monisha Srivastava, Mahima Singh, Jitendra Maurya, Rajesh Kumar Computer Science and Technology Khalsa College of Engineering and Technology Amritsar India Department of Computer Applications KIET Group of Institutions Ghaziabad India Computer Science and Engineering Chandigarh University Punjab India College Uttarakhand Dehradun India Department of Computer Science Engineering SRM Institute of Science and Technology UP Ghaziabad India MCA ABES Engineering College Ghaziabad India

Out of the most entrusted technological revolutions applied for the advancement of living standards and creation of a more convenient operation in the professional world, the Internet of Things (IoT) has been progressing at the highest projected pace. The interconnectivity of gadgets and devices over the internet to produce smarter control, communication, and a swift and easy lifestyle makes it an amazing tech introduction. However, the IOT also brings along a huge flow of privacy and security concerns whilst considering the huge inflow of data breaches and cyberattacks in the network data. The advancements in the security protocols of the data thus bring along mistrust among the users of IoT networks who might then no longer wish to be a part of this technological revolution. Therefore, it is important to maintain security assurance in the IoT based connection networks of wearable gadgets. This paper discusses the symmetric and asymmetric approaches of encryption over IoT which can help protect the network of smart phones connected to the wearable to assure a safe and smart data commute and avoid access to private data over the same internet connection. © 2021

关键词： Network security

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Innovations in Agri-Tech: A Review of Artificial Intelligence applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Skin Cancer Classification Using Convolutional Neural Network with DWT Features 14

Skin Cancer Classification Using Convolutional Neural Networ...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Pandiyan, M. Sivakumar, S. Nayak, Soumya Ranjan Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications KTR Campus Chennai Kattankulathur603203 India School of Computer Engineering Kiit Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350344837

The universal climate change and the ultraviolet radiation from the sun damage the skin cells. The damaged cells that could not be repaired will develop into skin cancer. The worldwide skin cancer resides in the top three positions of leading death cancer. Over the recent decades the death ratio of skin cancer is increasing. Thus, prediction and classification of the disease at early stages is mandatory for the patients survival. The artificial intelligence techniques used to automate the system that detects the skin lesions. The deep learning based convolutional neural network (CNN) approaches evidence on an automatic recognition of skin cancer more accurately. In this proposed method, the Discrete Wavelet Transformation (DWT) based features extraction techniques employed with CNN for skin cancer classification. The DWT method is used to extract the principal features from the skin cancer images after those optimal features are fed into CNN with softmax function, used to classify different types of skin cancers. This proposed method classifies different types of skin cancer more accurately. To analyze and evaluate the performance of the proposed model is tested with HAM 10000 dataset, which produces higher accuracy then the existing machine learning models. © 2024 IEEE.

关键词： Discrete wavelet transforms

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BuddyBot: AI Powered Chatbot for Enhancing English Language Learning 2

BuddyBot: AI Powered Chatbot for Enhancing English Language ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Karnati, Sai Bhargav Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences. © 2024 IEEE.

关键词： Sentiment analysis

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Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT 9

Monitoring, Tracking and Fighting Pandemics using Drone-base...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, K. Praveen Sangeetha, S. Kumar, T Rajesh Bhuvaneswari, R. Rasi, D. Mahaveerakannan, R. Kakatiya Institute of Technology and Science Department of Information Technology Warangal India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Computer Science and Applications Vellore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

关键词： Coronavirus

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

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

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

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Optimization of Network Mapping for Screening and Intrusion Sensing Devices 1

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Atri, Haritima Sharma, Amisha Mehrotra, Tushar Saxena, Sandeep Department of Computer Science and Applications School of Engineering and Technology Sharda University UP Greater Noida India Uttar Pradesh Greater Noida India

ISBN: (数字)9789819706419

ISBN: (纸本)9789819706402

Many of today’s cyberattacks heavily rely on screening addresses over the World Wide Web to find susceptible hardware and software. By keeping track of this scanning activity, you may assess the present situation with regard to several vulnerabilities and their exploitation. Studies examining scanning activity to this point have relied on uninvited traffic obtained from darknets and have concentrated on random screens of the address space. We suggest a method for identifying network scanning threats from both inside and outside the network. Our approach operates on the presumption that every legitimate connection between two computers must typically be accompanied by a DNS resolution;hence, any transfer that is not followed by a DNS inquiry is considered to be an inspection, unless it is permitted by the network’s security protocol. Effective port inspection and sniffing of packet technologies are a must for ensuring strong security. Wiremap, a Python-based port monitoring and packet sniffing programme that makes use of the Scapy library, is introduced in this study. Network administrators, security experts, and penetration testers may evaluate the security posture of their networks using Wiremap’s complete solution. Wiremap’s main goal is to give users a simple, effective tool that lets them scan a target IP address to look for accessible ports and record network data for study. The application takes advantage of the Scapy library’s robust sniffing and packet-dissection capabilities, giving users insights into transmission patterns and possible weaknesses in their networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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