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Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-dept. and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

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Triple-Node-Upset Recoverable Radiation-Hardened by Design Latch

Triple-Node-Upset Recoverable Radiation-Hardened by Design L...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Paparsenos, Evangelos Tsiatouhas, Yiorgos Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

CMOS technology evolution enhances integrated circuits (ICs) performance characteristics at the cost of their increased susceptibility to radiation and thus to the occurrence of single-event upsets (SEUs) that may lead to soft errors generation. SEUs may affect multiple nodes in a circuit (multiple node upsets - MNUs). Fortunately, these disturbances do not cause permanent damage to the circuits. Various techniques have been proposed to deal with SEUs that concurrently affect one, two or three nodes. In this paper, we propose the design of a latch that offers tolerance up to the level of triple-node upsets (TNUs). This is achieved by using redundancy in the hardware which provides the ability to store a logic value in multiple nodes within the latch as well as by using multiple feedback paths which allow it to recover its correct state in the event of an SEU. Compared to other techniques dealing with the same problem, our proposed method provides faster recovery time (higher than 16.87%) after an SEU, and at the same time reduced power consumption and higher speed performance. © 2024 IEEE.

关键词： Radiation hardening

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Principal Component Analysis and Deep Learning-Based Traffic Anomaly Detection in WSN

Principal Component Analysis and Deep Learning-Based Traffic...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Shakya, Vandana Choudhary, Jaytrilok Singh, Dhirendra Pratap Manit Dept. of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350348460

Wireless Sensor Networks (WSNs) have advanced quickly due to the fast expansion of wireless networks. Yet, because of their ease of use and versatility, security concerns have grown. This means that conducting research on intrusion protection in WSNs is now essential. Denial of Service (DoS) assaults are among the most common types of network attacks. They are dangerous because they take down the target network in order to accomplish their goal. Within WSNs, where devices function with limited resources, a denial-of-service attack has the potential to be disastrous. This research suggests a novel solution for WSNs, which are susceptible to assaults because to their devices' little storage capacity. To find abnormalities in DoS traffic within WSNs, the technique combines a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA). By detecting and reducing the effects of DoS assaults, and by utilising the complementary capabilities of PCA and DCNN in this particular situation, the goal is to improve the security of WSNs. Compared with other traditional DL architectures, the proposed model has a more simplified structure and better feature extraction capabilities. This special combination gives it the power to quickly identify anomalous network activity in WSNs devices, especially those with limited storage. Because of its lightweight design, the suggested model addresses the inherent resource limits and guarantees optimal performance in the context of WSNs. A variety of assessment measures, such as confusion matrices, different classification metrics, and Receiver Operating Characteristic (ROC) curves, are used to verify the effectiveness of the suggested model. These metrics are used to evaluate the model's categorization performance in a rigorous manner. Extensive experimental comparisons reveal that the small size of the proposed model outperforms other popular models for anomalous traffic detection with regards to classification performance

关键词： Deep Convolutional Neural Network (DCNN) Denial of Service (DoS) KNN Principal Component Analysis (PCA) WSNs

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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

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NFT Marketplace: What are NFTs, and how does OpenSea succeed in acquiring the most of the NFT Space 1

NFT Marketplace: What are NFTs, and how does OpenSea succeed...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Thakur, Anil Divya, K. Verma, Sahil Kaur, Maninder Chandigarh University Dept. of Computer Science & Engineering Mohali India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Guru Gobind Singh College for Women Dept. of Computer Science & Applications Chandigarh India

ISBN: (纸本)9798350373042

Nowadays, the world is becoming smarter, so as the technology, the new era is on the boom, which is blockchain because of its different use cases and benefits. The blockchain is a growing list of records called blocks that are linked together with the use of cryptography. Because all servers on a network are corresponding and a significant concern in the types of network style, its data sources are managed through a peer-to-peer (p2p) network. The servers engage in negotiation and communication through the agreement method. The dispersed network is a network system in which information, software application, and computer system programs are spread out on greater than one node (computer systems). These nodes also depend on each other. It is only a p2p network that gets rid of a solitary factor of failing. It is a decentralized, dispersed, and unalterable journal to show electronic purchases. One of the concepts of blockchain is NFTs. These are digital assets with a unique identity and cannot be replicated. They can be used for a variety of purposes, such as in-game items, digital collectibles, or even to represent real-world assets like cars or houses. They can represent any digital asset or physical goods. NFTs can represent ownership and authenticity of any type of digital asset or physical goods. They can also represent a stake in the game, such as ownership in a casino or a share in a company. © 2024 IEEE.

关键词： Smart contract

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Smart Garden Watering and Lighting System Integrated with Machine Learning Model to Maintain Urban Gardens

Smart Garden Watering and Lighting System Integrated with Ma...

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

作者： Shahapure, Arsheyee Siddha, Rishika Tewary, Tanmay Shahapure, Aniruddha Mantri, Shamla Kamble, Aparna Dept. Computer Science and Engineering MIT-WPU Pune India

ISBN: (纸本)9798331512965

Garden maintenance may be a difficult task, particularly in urban settings. Many people are not skilled enough to determine their plants' precise requirements for lighting and watering. Conventional gardening techniques frequently result in underwatering or overwatering, wasting resources, and producing unhealthy plants. Our completely automated smart garden watering and lighting system offers a fully automated solution to these problems. With its sophisticated sensors, this system keeps an eye on the quantity of moisture in the soil and the amount of sunshine it receives, making sure plants get the correct amount of both for healthy growth. Traditional gardening involves guesswork and physical effort, which our solution avoids by automating the irrigation and lighting operations. You may have the advantages of a flourishing garden without the trouble of ongoing upkeep with our intelligent gardening system. Our method offers a dependable and effective solution to care for your plants and improve your urban green space, regardless of experience level. © 2025 IEEE.

关键词： Support vector machines

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Enhancing Artificial Intelligence and Machine Learning Understanding Through EnVision: A Virtual Reality Approach 1

Enhancing Artificial Intelligence and Machine Learning Under...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Patra, Louise Kumari, Manisha Gopalapillai, Radhakrishnan Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350365016

The quality of teaching and learning process can be improved through innovative methods like use of virtual reality. We introduce EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education. EnVision offers a unique approach where users build models through immersive learning experiences. Using interactive tutorials mixed with real-time data integration and visualization, users learn AI/ML concepts with ease. Through qualitative and quantitative methods, we evaluate EnVision's effectiveness and user satisfaction, contributing to the advancement of innovative educational gaming technologies. The evaluation results show that EnVision provides the AI/ML practitioners engaging learning experiences. © 2024 IEEE.

关键词： Virtual environments

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IoT Security: A Deep Learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kauhsik, Bhanu Nandanwar, Himanshu Katarya, Rahul Delhi Technological University Dept. of Software Engineering Delhi India Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350313413

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage Machine Learning and Deep Learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse. © 2023 IEEE.

关键词： Cyberattack Deep Learning Internet of Things (IOT) Intrusion Detection System (IDS) Network Security

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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Deep Learning Model for Detecting the Presence of Neurological Conditions in MRI and CT Scans 2

Deep Learning Model for Detecting the Presence of Neurologic...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Kanchan, Jayapadmini Ananya, H.N. Anoksha, N.A. Hegde, Sarika Dept. of Computer Science and Engineering Nitte Karkala India

ISBN: (纸本)9798350354461

An AI-powered MRI/CT analysis solution for medical professionals can significantly enhance diagnostic accuracy and efficiency. By harnessing deep learning algorithms, this solution can minimize human error and accelerate image interpretation. The incorporation of a Convolutional Neural Network (CNN) model empowers the solution to identify abnormalities in MRI/CT images with high consistency and reliability. This solution supports intricate case evaluations and integrates effortlessly with electronic health records, making high-quality diagnostic capabilities available to a wider range of healthcare professionals. The solution is meticulously designed to detect various abnormalities, including tumors, Alzheimer's, and hemorrhages from MRI images, as well as COVID-19 and checking for squamous conditions from CT images. Ultimately, this solution aims to improve patient outcomes through precise and prompt diagnoses, while also serving as an educational resource and contributing to ongoing medical research and innovation. The brain tumor MRI model achieved an accuracy of 87.5% after 100 epochs, while the chest CT scan model attained 95.65% accuracy in just 50 epochs. Brain hemorrhage MRI model accomplished 88.45% accuracy in 30 epochs, and the COVID-19 dataset model reached 78.9% accuracy in 40 epochs. Lastly, the Alzheimer's model obtained an accuracy of 80.70% after 40 epochs. © 2024 IEEE.

关键词： Convolutional neural networks

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