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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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Agro-smart Crop Recommendation and Yield Forecasting Employed through Machine Learning

Agro-smart Crop Recommendation and Yield Forecasting Employe...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Kollu, Praveen Kumar Raavi, Niranjani Duggineni, Dharani Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350375442

In India, farmers often face significant challenges in selecting the appropriate crop variety for specific soil types, leading to substantial productivity losses. This project leverages precision agriculture by utilizing specific information about soil characteristics, location, and planting times to design a sophisticated model. The model employs Naïve Bayes for crop recommendation and Linear Regression for yield prediction. The model achieved a 99.54% accuracy in crop recommendation and an R2 of 0.94 in yield prediction, outperforming traditional methods. The implementation of high-end machine learning algorithms not only provides accurate crop recommendations but also predicts yields, equipping farmers with essential tools for informed decision- making to enhance productivity. The machine learning model is integrated into a user-friendly interface to facilitate its adoption. © 2024 IEEE.

关键词： Smart agriculture

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle 6

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ganguly, Souvik Pati Mandal, Kailash Guha, Debasis Dr. B.C. Roy Engineering College Dept. of Computer Application Durgapur India National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350306613

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively. © 2024 IEEE.

关键词： Bi-directional GRU Cognitive Deficits Cognitive Equilibrium Learner's Mental State Meta-heuristic Optimization SFLA

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Singh, Ratan Tejnarayan Wasnik, Aarush Mistry, Krishna Modi, Jayen Koshti, Dipali Fr. Conceicao Rodrigues College of Engineering Dept. of Electronics and Computer Science Mumbai India

ISBN: (纸本)9798331512965

A novel smart mirror system is proposed to enhance users' daily routines by integrating advanced technologies. Utilizing a Raspberry Pi, an electronic display, a two-way mirror, a camera, and control mechanisms powered by OpenCV and MediaPipe, the system provides personalized interactions through facial recognition. This feature allows the smart mirror to identify users and tailor its responses accordingly. Beyond its aesthetic appeal, the system functions as an interactive fitness coach, guiding users through workouts, correcting their form, and delivering real-time feedback. This paper explores the potential of smart mirrors to revolutionize personal fitness and care, offering an innovative approach to interactive user engagement. © 2025 IEEE.

关键词： Face recognition

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A Comprehensive End-to-End Solution for Web Security with Cryptography, Multi-Factor Authentication, and Secure Communication 3

A Comprehensive End-to-End Solution for Web Security with Cr...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Kanderi Johith Sai, Kandlapalli Aravind Reddy, Adapala Dharshan Reddy, Chinthamreddy Pranay Daiwik Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In today’s digital landscape, securing sensitive information has become an essential priority in the modern digital era for both individuals and organizations. This project introduces a comprehensive web application that integrates advanced encryption and decryption functionalities alongside multifactor authentication (MFA) and secure payment processing. The application supports various cryptographic algorithms, including AES, DES, Triple DES, Blowfish, etc., allowing users to securely encrypt and decrypt data through dedicated routes accessed over secure HTTPS connections. To further enhance security, robust multifactor authentication mechanisms, such as OTP-based challenge-response and QR-code-enabled 2FA, have been implemented to prevent unauthorized access to sensitive operations. The platform also incorporates PGP encryption powered by Keybase, enabling users to securely exchange encrypted emails. Additionally, a secure payment gateway is integrated using Stripe, ensuring sensitive payment data is encrypted and handled safely. With a strong focus on data integrity, user privacy, and modern web security standards, this project delivers an all-encompassing solution for safeguarding sensitive information and facilitating secure communication. By addressing common vulnerabilities and security risks, the platform stands as a reliable and scalable tool for data protection. © 2025 IEEE.

关键词： Authentication Protocol

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms 15

Machine Learning Techniques for Identifying Child Abusive Te...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Uddin, Mohammad Kamal Aziz, Mohammad Tarek Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention. © 2024 IEEE.

关键词： Deep Learning Machine Learning Natural Language Processing Sentiment Analysis Supervised Model

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