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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swetha, P. Senthilkumar, J. Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

This research study proposes a comprehensive Crop Recommendation System that uses advanced machine learning to boost agricultural productivity. By analyzing key environmental and soil factors, including soil composition, pH, temperature, humidity, and rainfall, the system offers customized, data-driven crop suggestions to optimize yield and promote sustainable farming. The system evaluates several algorithms, including Naive Bayes, Decision Tree, Logistic Regression, and Random Forest, and identifies Naive Bayes as the top performer for accurately predicting crop suitability. This multi-algorithm approach represents a significant advancement in precision agriculture, empowering farmers and policymakers with actionable insights to drive improvements in the agricultural sector. Leveraging historical data, the Crop Recommendation System supports farmers, consultants, and policymakers in making more informed and strategic crop selection decisions. The system's precision and adaptability make it a valuable resource for stakeholders focused on improving resource efficiency and fostering sustainable agriculture, ultimately enhancing food security and minimizing environmental impact. © 2025 IEEE.

关键词： Agriculture Crop Recommendation System Decision Tree Logistic Regression Machine Learning Naive Bayes Random Forest

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A Study on Machine Learning Techniques for Precision Medicine Recommendation

A Study on Machine Learning Techniques for Precision Medicin...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sugavaneshwari, P. Saranya, K.G. Government College of Engineering Department of Computer Science and Engineering Dharmapuri India PSG College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Precision medicine is emerging now provides decision making for disease treatment and prevention with the help of genome, environment and lifestyle. This study on precision medicine recommendation system mainly focuses to explore the different optimal techniques to improve and meet the objectives of the precision medicine recommendation. It focuses on the different feature selection techniques and the prediction models for effective diagnosis of the disease, improve the survival rate of the patients and also to suggest the best lifestyle practice to carryover. © 2023 IEEE.

关键词： Decision making

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Does computer-Based Learning Suits Children's Desire to Control Their Own Learning Experience in Ghana?

Does Computer-Based Learning Suits Children's Desire to Cont...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Amponsah, Rebecca Adwoa Doe, Martin Sebuabe, Samuel Yao Asare, Michael Tetteh Arthur, John Kingsley Damoah, Dominic Valley View University Computer Science and Engineering Department Oyibi Ghana

ISBN: (纸本)9798350376838

In Ghana's educational sector, the integration of ICT has changed traditional pedagogical ways hence introducing computer-based learning (CBL) as a powerful tool in controlling children's desire to enhance their learning experience. This paper explores the involvement of CBL with children's intrinsic motivation to manage their own experience in learning. In achieving this, purposive sampling technique was used throughout, and the results obtained was subjected to a simple descriptive statistical analysis. The results show that CBL technologies suit the ability of children to control their learning experiences with immediate feedback, motivation, memory retention, and acquisition of new and general knowledge. © 2024 IEEE.

关键词： Federated learning

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SCOUT+: Towards Practical Task-Driven Drivers' Gaze Prediction 35

SCOUT+: Towards Practical Task-Driven Drivers' Gaze Predicti...

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35th IEEE Intelligent Vehicles Symposium, IV 2024

作者： Kotseruba, Iuliia Tsotsos, John K. York University Department Of Electrical Engineering And Computer Science Toronto Canada

ISBN: (纸本)9798350348811

Accurate prediction of drivers' gaze is an important component of vision-based driver monitoring and assistive systems. Of particular interest are safety-critical episodes, such as performing maneuvers or crossing intersections. In such scenarios, drivers' gaze distribution changes significantly and becomes difficult to predict, especially if the task and context information is represented implicitly, as is common in many state-of-the-art models. However, explicit modeling of top-down factors affecting drivers' attention often requires additional information and annotations that may not be readily *** this paper, we address the challenge of effective modeling of task and context with common sources of data for use in practical systems. To this end, we introduce SCOUT+, a task-and context-aware model for drivers' gaze prediction, which leverages route and map information inferred from commonly available GPS data. We evaluate our model on two datasets, DR(eye)VE and BDD-A, and demonstrate that using maps improves results compared to bottom-up models and reaches performance comparable to the top-down model SCOUT which relies on privileged ground truth information. Code is available at https://***/ykotseruba/SCOUT. © 2024 IEEE.

关键词： Forecasting

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IOT BASED SMART IRRIGATION SYSTEM USING DUAL AXIS SOLAR TRACKER 2

IOT BASED SMART IRRIGATION SYSTEM USING DUAL AXIS SOLAR TRAC...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Madhu, B.G. Manoj, H.K. Belavadi, Arun Tejas, C.M. Chinthan, H.K. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

In agriculture, proper water management is important for crop growth and resource conservation. This brief describes a smart water system designed to optimize water use by integrating dual-axis solar trackers. The system combines renewable energy sources with smart water management to increase environmental sustainability and agricultural performance. To get stronger every day. This makes the use of solar energy for water supply efficient, continuous and reliable. The saved energy is stored in batteries that provide reliable power for the equipment. Sensors constantly monitor humidity and send the information to the central control center in a timely manner. When soil moisture drops below the threshold, this indicates the need for irrigation, control room pump. This allows remote monitoring and control of water flows via a mobile app or web interface. Farmers have access to real-time information on soil moisture and processes, so timely adjustments and interventions can be made anytime, anywhere. Combining renewable energy with smart water management, the system offers a solution for agricultural water management. This brief demonstrates the potential of integrating solar technology into smart water systems, helping to improve efficiency and increase yields in modern agriculture. ©2024 IEEE.

关键词： Soil moisture

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Optimizing Automatic Test Case Generation Using Singular Value Decomposition Approach 15

Optimizing Automatic Test Case Generation Using Singular Val...

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

作者： Bisi, Manjubala Srivastava, Aryan Verma, Abhishek Kumar, Aditya National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350370249

Test case generation is a complex problem when dealing with multiple objectives, which hinders search efficiency with algorithms like DynaMOSA in the EvoSuite framework. To address this issue, we propose SVD-DynaMOSA, utilizing Singular Value Decomposition for dimensionality reduction. This approach aims to optimize the test case generation process, leading to improved efficiency. Experimentation with 49 projects and 110 classes from the SF110 dataset, selected for their complexity and number of objectives, demonstrates SVD-DynaMOSA's superiority over DynaMOSA. It achieves higher or comparable coverage in multiple metrics, enhancing test case generation performance significantly. SVD-DynaMOSA shows promise in producing comprehensive test suites in practical time frames, overcoming the limitations of current methods. © 2024 IEEE.

关键词： EvoSuite Many-objective search optimization Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Test case generation

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A Machine Learning Approach With PSO for Intrusion Detection

A Machine Learning Approach With PSO for Intrusion Detection

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sharma, Riddhi Madar, Mohd Kamil Meena, Mahak Kumar, Vinod Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350375237

With the advent of internet and emergence of a digital landscape in the present world, sharing information online is prevalent. While this has made life much easier by facilitating speedy responses and widespread connectivity, it comes with its own set of challenges, the most concerning being threats to network security. The incessant threats to confidentiality of the information calls for measures to protect sensitive data. Network Intrusion Detection Systems have proved to be fruitful in monitoring malicious activities and thereby ensuring security of data by preventing unauthorized access in the systems. In this study, multiple machine learning classifiers were utilized to analyze the NSL-KDD dataset for network intrusions. DoS and Probe subsets were used and attempts were made to boost the performance of the machine learning models. Addressing class imbalance problem with the aid of SMOTE was crucial in stimulating unbiased model training. Machine learning algorithms like Support Vector Machines, K-Nearest Neighbors, Random Forest, XG Boost and Decision Trees were employed to train the models. The accuracies of the models were compared before and after implementing Particle Swarm Optimization for hyperparameter tuning. © 2024 IEEE.

关键词： Network security

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Evaluating Neural Network Parameter Obliteration in Catastrophic Forgetting

Evaluating Neural Network Parameter Obliteration in Catastro...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Sehra, Udit Dutta, Rishi Raj Pandit, Mohammad Khalid National Institute of Technology Hamirpur Department of Computer Science & Engineering India

ISBN: (纸本)9798350364866

One of the major weaknesses of Neural Networks is their inability to learn multiple functions (tasks) sequentially. This phenomenon is observed when a data distribution shift is encountered in continual learning, leading to catastrophic forgetting. Such ability is foremost important to develop models with general intelligence. In this paper, we evaluate how different parts of the neural network contribute to overall forgetting by investigating the training dynamics during different methods of continual learning. We evaluate and analyze the learning dynamics in catastrophic forgetting on frequently used classification models on CIFAR-10 and Fashion-MNIST datasets. Our findings reveal a consistent trend in continual learning scenarios, both in domain-incremental and class-incremental settings. Specifically, gamma means demonstrate stability in earlier layers but exhibit increased plasticity in later layers, indicating heightened adaptability to changes in data distributions. © 2024 IEEE.

关键词： Neural network models

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Near Optimal Locality Sensitive Orderings in Euclidean Space 40

Near Optimal Locality Sensitive Orderings in Euclidean Space

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40th International Symposium on Computational Geometry, SoCG 2024

作者： Gao, Zhimeng Har-Peled, Sariel Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Department of Computer Science University of Illinois UrbanaIL United States

ISBN: (纸本)9783959773164

For a parameter Ε ∈ (0, 1), a set of Ε-locality-sensitive orderings (LSOs) has the property that for any two points, p, q ∈ [0, 1)d, there exist an order in the set such that all the points between p and q (in the order) are Ε-close to either p or q. Since the original construction of LSOs can not be (significantly) improved, we present a construction of modified LSOs, that yields a smaller set, while preserving their usefulness. Specifically, the resulting set of LSOs has size M = O(Ed−1 log E), where E = 1/Ε. This improves over previous work by a factor of E, and is optimal up to a factor of log E. As a consequence we get a flotilla of improved dynamic geometric algorithms, such as maintaining bichromatic closest pair, and spanners, among others. In particular, for geometric dynamic spanners the new result matches (up to the aforementioned log E factor) the lower bound, Specifically, this is a near-optimal simple dynamic data-structure for maintaining spanners under insertions and deletions. © Zhimeng Gao and Sariel Har-Peled.

关键词： Structural optimization

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An Efficient Scalp Inspection and Diagnosis System Using Multiple Deep Learning-Based Modules un système efficace d'inspection et de diagnostic du cuir chevelure utilisant plusieurs modules basés sur l'apprentissage profond

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and computer engineering 2024年第1期47卷 22-35页

作者： Chen, Liang-Bi Chang, Wan-Jung Chiu, Yi-Chan Huang, Xiang-Rui National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan Southern Taiwan University of Science and Technology Department of Electronic Engineering Tainan710 Taiwan

The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous (2). These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous (2) excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively);Faster-R-CNN-Inception-ResNetV2-Atrous (4) outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%);and Faster-R-CNN-Inception-ResNetV2-Atrous (4) is the best-performing method overall. © 2021 IEEE.

关键词： Inspection

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