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Revolutionizing Mango Crop Health: A Deep Learning Approach for Precise Identification of Leaf Diseases 10

Revolutionizing Mango Crop Health: A Deep Learning Approach ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Navadharani, N. Mouthami, K. Devipriya, R. Anandamurugan, S. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798350384369

Plant diseases pose significant challenges to the agricultural sector, leading to decreased crop production and financial losses. Mango, being a popular fruit worldwide, is susceptible to various leaf diseases. Traditional diagnosis of these disorders requires labor-intensive and time-consuming manual picture interpretation. To address this issue, deep learning models have been utilized to enhance the precision and effectiveness of identifying leaf illness in mango crops. In this study, ResNet50, ResNext50, Convnet, and Conventv2 deep learning models were compared to find the most successful approach. The results demonstrated that the Conventv2 model achieved the highest accuracy of 98.21%. The major aim of the research work is improvising sustainable agricultural practices by leveraging advanced technology for accurate plant disease classification. The findings have implications for improving mango crop management and ensuring a more sustainable and efficient agricultural industry. © 2024 IEEE.

关键词： Fruits

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Improving Accuracy for Cat Detection Utilizing Learning Methods 12

Improving Accuracy for Cat Detection Utilizing Learning Meth...

引用

12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ishihara, Hideyasu Goto, Tomio Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

To make it easier to identify the number of cats, we propose a cat detection system using YOLOv7. To demonstrate the effectiveness of our proposed method, a comparison experiment is conducted with a system using YOLOv... 详细信息

ISBN: (纸本)9798350340181

关键词： Object detection

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Decision Support System for Crop Management using Convolutional Neural Network 3

Decision Support System for Crop Management using Convolutio...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Immaculate Mercy, A. Ruby, D. Logesh, T. Shahidha Shireen, S. Periyar Maniammai Institute of Science & Technology Dept. of Computer Applications Tamilnadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Effective crop management is critical for enhancing agricultural productivity and sustainability. Traditional farming methods often face challenges in accurately forecasting yields, optimizing resources, and determining the right harvest timing. To address these challenges, this study develops a decision support system for rice farming in South India by utilizing deep learning techniques, specifically convolutional neural networks (CNN s), combined with dimensionality reduction methods. The system uses a comprehensive dataset that includes factors such as rainfall, temperature, soil type, and rice variety to predict rice yield. Additionally, it optimizes the use of resources such as water, fertilizer, and labor on the basis of expected yields. The proposed CNN model, coupled with squared exponential kernel PCA (SEKPCA), achieved a prediction accuracy of 83.12% and an F1 score of 0.63, enabling farmers to make informed decisions and improve productivity. The system also evaluates resource requirements, offering insights into water usage, fertilizer, and labor needs. This approach enhances agricultural efficiency, reduces waste, and promotes sustainable farming practices. By integrating advanced deep learning technologies into traditional agriculture, the system contributes to more precise yield predictions and better resource management, paving the way for improved crop management and sustainability in rice farming in South India. © 2025 IEEE.

关键词： Resource allocation

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Enhancing Lymph Node Classification in Rectal Cancer MRI Images Through a Multi-Model Approach

Enhancing Lymph Node Classification in Rectal Cancer MRI Ima...

引用

2024 IEEE International Conferences of AI, Science, Engineering, and technology, AIxSET 2024

作者： Gupta, Rishit Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Bhatt, Manish Kijsirikul, Boonserm Wang, Aili Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Chulalongkorn University Dept. of Computer Engineering Bangkok Thailand Harbin University of Science and Technology Higher Educational Key Laboratory Harbin China Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350390995

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI Approach for Minimizing the Energy Consumption of Servers Using Deep-Q-Learning 8

AI Approach for Minimizing the Energy Consumption of Servers...

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Kaulage, Anant Shaha, Shraddha Naik, Tanaya Nikumbh, Khushi Jagtap, Vedant Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350334012

This paper focuses on minimizing energy consumption by servers in data centers. Server's energy consumption can be impacted by numerous factors, such as the number of connected devices, the workload being processed, and the energy efficiency of the *** energy consumption can be serious because of several reasons as it can impact the reliability of servers because high temperatures generated by energy consumption can lead to hardware failure and other technical issues. Therefore, reducing energy consumption in servers is important for improving the cost-effectiveness, sustainability, scalability, and reliability of data center operations. A type of reinforcement learning called Deep Q-Learning (DQL) can be used to address issues with server energy consumption. The basic idea behind DQL is to train an artificial agent, such as a neural network, to make decisions about energy consumption in real time. The agent is trained by frequently performing actions in a setting and earning rewards depending on the amount of energy consumed by specific actions. Over time, the agent learns which actions are most likely to lead to energy savings, and it can then be deployed to make real-time decisions about energy consumption in a server. Experimental results of the proposed research show an average of 66% power saving in the server's consumption of energy using Deep Q-Learning (DQL). © 2023 IEEE.

关键词： Energy utilization

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Secure Authentication in the Presence of Malicious Messages and Packet Reorders: Study on CAN Bus 15

Secure Authentication in the Presence of Malicious Messages ...

引用

15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Maragkou, Sofia Grammatikakis, Miltos Papatheodorou, Nikos Jantsch, Axel Institute of Computer Technology Vienna University of Technology Vienna Austria Hellenic Mediterranean University Ece Dept. Heraklion Greece

ISBN: (纸本)9798331507695

Message authentication is fundamental for securing modern automotive networks. Our work focuses on integrating buffering in existing authentication protocols to sustain the presence of malicious or corrupt messages, and arbitrary packet swaps in the in-vehicle network. The proposed extension applies to the popular vatiCAN protocol, and other CAN bus authentication protocols, which use separate messages for transferring packet information and authentication data. The proposed extension uses one or more, independent Finite State Machines (FSMs) at each receiver node to temporarily store and subsequently validate message pairs, i.e., a legitimate data packet L with its hashed-based message authentication code (HMAC) packet H. The proposed methodology is evaluated experimentally on a Raspberry Pi-based Electronic Control Unit (ECU) with CAN interfaces. We examine key design parameters, such as the LH swap rate, the malicious rate, and queue configuration options, such as the queue size and flush policy. Results show that the protocol extension improves authentication. When the queue size is below 5, the LH swap rate is up to 50%, and 50% of malicious packets are introduced, the validated packet rate is low (5%). However, if the queue size exceeds 20, the verified packet rate reaches 100%, regardless of other parameters. The increased queue size has a minimal effect on latency, which increases by a few ms on average, and on false positives, which remain below 10-9. Statistical models help evaluate queue size bounds for worst-case scenarios, strengthening our experimental findings. © 2025 IEEE.

关键词： Authentication Protocol

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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Explainability of Artificial Intelligence Systems: A Survey

Explainability of Artificial Intelligence Systems: A Survey

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Hossain, Mazharul Das, Saikat Krishnamurthy, Bhargavi Shiva, Sajjan G University of Memphis Dept. of Computer Science Memphis United States Utah Valley University Dept. of Computer Science Orem United States Siddaganga Institute of Technology Dept. of Computer Science Karnataka Tumakuru India

ISBN: (纸本)9798350335590

Complex machine learning (ML) algorithms consistently outperform traditional ML models, yielding significantly improved results. Furthermore, these complex system uses large amounts of data with many features to train, increasing their predictive power but reducing our ability to explain them. Thus, complex ML models primarily operate like black boxes. Explaining their inference processes and predictions poses a challenge due to the necessity to account for millions of interacting weights in a complicated way. Still, we need to clarify their inner workings and procedures, as understanding a model can improve transparency and promote trustworthiness. Therefore, 'Explainability' assumes pivotal importance, as it enhances accuracy and broadens the scope of applicability for complex ML methods within critical domains. Thus, in this review paper, we have investigated the complete ML system development lifecycle, compiled recommendations to cover explainability gaps from diverse literature sources, listed different methods of interpretability and explainability, and improved our explainability-augmented ML system development life cycle for all stakeholders to enhance trustworthiness. © 2023 IEEE.

关键词： Transparency

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through Automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 IEEE.

关键词： Sales

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Factors on the UTAUT2 to Identify the Influence on the Usage Behavior of AI-Based Air Quality 3

Factors on the UTAUT2 to Identify the Influence on the Usage...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Aini, Qurotul Hartomo, Kristoko Dwi Dwi Purnomo, Hindriyanto Wahab, Abdul Rahardja, Untung Satya Wacana Christian University Dept. of Computer Science and Technology Salatiga Indonesia Satya Wacana Christian University Dept. of Information Technology Salatiga Indonesia International Islamic University Malaysia Dept. of Information and Computer Technology Kuala Lumpur Malaysia University of Raharja Master of Information Technology Tangerang Indonesia

ISBN: (纸本)9798350367492

This study investigates the effect of AI-based air quality detection apps on user behavior in Indonesia, ranked 26th globally for air pollution. Utilizing an online survey with 287 participants, focusing on 156 active users, and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research assesses user satisfaction in environmental monitoring using the Unified Theory of Acceptance and Use of technology 2 (UTAUT2). This model examines 9 key variables: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), Habit (HT), Behavioral Intention (BI), Usage Behavior (UB), Considered Risk (CR), and Considered Trust (CT). Key findings emphasize the crucial roles of PE and BI in determining user's usage intentions and behaviors. These insights are vital for enhancing air quality monitoring technologies for developers and policymakers. The study also identifies areas for future research, such as exploring external and demographic influences, to further understand user behavior and satisfaction with AI-based environmental tools. Overall, it highlights the importance of user-centric approaches in technological development for improving environmental health outcomes. © 2024 IEEE.

关键词： Environmental monitoring

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