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Detection of Maize Plant Insects using Lightweight Deep-learning Scheme 3

Detection of Maize Plant Insects using Lightweight Deep-Lear...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Vickram, A.S. Bhavani Sowndharya, B. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Automatic plant condition monitoring using a chosen computer algorithm is one of the recent advancement in the agriculture domain. To ensure the plant health, it is necessary to monitor the pest which causes the considerable damage to the plant. This work considered the monitoring of maize (corn) from the study and the common pests which causes the severe damage to maize, like the grasshopper and beetle are analyzed automatically using a chosen Lightweight Deep-learning (LD) methodology. This work considered the MobileNet (MN) variants for the study and this proposed technique consist the following phases;digital image collection and converting it to 224x224 pixels, feature extraction using chosen MN-model, feature optimization using Butterfly-Algorithm (BA) and fused-features generation, and binary classification with 5-fold cross validation. This work considered the MN-variant model to examine the pest images and the achieved result confirms that this scheme presents a detection accuracy up to 96% when SoftMax based classification is executed. © 2025 IEEE.

关键词： Smart agriculture Condition monitoring Accuracy Databases Plants (biology) Digital images Insects Feature extraction Monitoring Optimization

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Application of computer vision technology in interactive teaching tools

Application of computer vision technology in interactive tea...

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2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Zhang, Suping Guangzhou Huashang Vocational College Guangzhou511300 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As the digital age progresses, the intersection of computer vision technology and education is drawing significant attention, enabling the development of interactive teaching tools that redefine how knowledge is imparted. While these tools promise to enhance the educational experience through increased engagement and personalized learning pathways, a critical enabling component is the ability to swiftly and reliably detect and interpret educational content and student interactions. This paper puts forth an improved object detection paradigm within this context using an advanced YOLO (You Only Look Once) algorithm modified for the unique requirements of interactive educational environments. We introduce a secondary feature fusion technique within the YOLO framework to enhance detection accuracy—key to maintaining the flow and interactivity of the educational experience. The innovative approach includes lightweight separable convolutions replacing traditional convolution modules to minimize network load and an enrichment of the DarkNet-53 backbone with dense connections for better feature propagation. In addition, we implement a dual-stage feature fusion strategy that consolidates the strengths of different network layers to ensure comprehensive feature extraction. Our proposed enhanced YOLO algorithm addresses the persisting challenge of precision in object detection to effectively facilitate the deployment of computer vision in interactive teaching, propelling the education sector towards a more engaging and sophisticated future. © 2025 SPIE.

关键词： Computer vision

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Optimizing DDoS Attack Detection Using Machine learning 2

Optimizing DDoS Attack Detection Using Machine Learning

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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Automatic Classification of Brain MRI slices into Glioma/Meningioma using Deep Transfer-learning 3

Automatic Classification of Brain MRI slices into Glioma/Men...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Vickram, A.S. Mathan Muthu, C.M. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Brain tumor (BT) is a harsh brain abnormality and early detection and treatment is essential to reduce the impact of the disease. This work proposed a Deep Transfer-learning (DT) approach to detect the Glioma/ Meningioma with a better accuracy. The stages of this work includes;image collection and modifying its dimension to 224x224 pixels, feature extraction using chosen DT-models, feature reduction and serial features integration to get the fused-features, and bi-level classification using SoftMax and verifying the merit using 5-fold cross validation. This work initially executed the detection using individual features and then the experiment is repeated using the fused-feature vector (FFV). The FFV based detection helps to achieve a detection accuracy >97% on the chosen task and this outcome confirms the clinical significance of the proposed technique. In this work, the detection is performed using only the axial-plane 2D MRI slides, and in future, it can be extended to clinical MRI examination task. © 2025 IEEE.

关键词： Image acquisition

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teaching Machine learning in K-12 Education: Potential and Challenges

Teaching Machine Learning in K-12 Education: Potential and C...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Del Pilar Aldana Bravo, Gissela Caballero, Jesus Emilio Agustin Padilla De La Cruz, Jhonn Robert Ruiz De Ruiz, Kathy Flores Cabrera Purguaya, Ruth Janet Toledo Cristobal, Obed Isaias Matias Universidad César Vallejo Escuela de Posgrado Lima Peru Universidad Nacional de Ucayali Pregrado Ucayali Peru Universidad César Vallejo Pregrado Lima Peru

ISBN: (纸本)9798331508685

A growing number of from kindergarten to twelve and colleges and universities are using machine learning (ML) methods, which have shown promise in a variety of computer domains. The study needs to comprehend how awareness and freedom are formed in these frameworks. However, one of the challenges facing computer education studies is incorporating artificial intelligence and classical mathematical thinking into the school curriculum. Research on how individuals learn to prepare, evaluate, enhance, and implement machine learning systems is lacking, particularly in the K-12 educational area, despite machine learning's pivotal role in today's technology. This article places current research in the computing learning setting. Outlines variations that K-12 computing instructors should consider when tackling this obstacle, and graphs arising pathways in educational reality, concept, and innovation related to instructing machine learning in schools in grades K-12. To properly include ML in the more comprehensive K-12 computer curriculum, the paper focuses on significant aspects of the perspective shift. © 2025 IEEE.

关键词： Adversarial machine learning

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Unmasking Hypertension Through a Fusion of Human Lifestyle Data using Machine learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

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ADA-Gen: Iterative and Incremental Generation of Full-Stack Apps for learning Agile/DevOps Software Development Practices 17

ADA-Gen: Iterative and Incremental Generation of Full-Stack ...

引用

17th international conference on Computer Supported Education, CSEDU 2025

作者： Duong, Ta Nguyen Binh School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9789897587467

To learn Agile/DevOps practices effectively, students need to apply them in an actual software development project. This is challenging if students are mostly from non-computing backgrounds and they do not have time in the curriculum to learn programming and related tools. Therefore, it is important to help students who do not possess programming foundations to develop fully functional software during the process of learning Agile/DevOps concepts. We noted that existing low-code/no-code app development platforms have not been designed to teach Agile/DevOps practices. On the other hand, recent AI-based tools for code generation such as GitHub Copilot have been built mainly for programmers. In this work, we designed and implemented ADA-Gen (Agile/DevOps App Generator), a teaching tool leveraging large language models (LLMs) to generate full-stack web apps following an iterative and incremental development methodology widely practiced in Agile/DevOps circles. ADA-Gen is integrated with Jira, one of the most popular platforms for software project management, so students can use it right away without much further setup. This approach allows non-computing students to experience the complete full-stack software development life cycle. We have conducted extensive evaluations of ADA-Gen using various realistic project scenarios. The evaluations demonstrated the capabilities of ADA-Gen in full-stack web app generation, and in providing plenty of learning opportunities for students to appreciate key Agile/DevOps practices. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Software design

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Exploration of Software Field Engineer Training Based on AI+Capability Model 24

Exploration of Software Field Engineer Training Based on AI+...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Junjie Wu, Qian School of Information Wuhan Software Engineering Vocational College Wuhan Open University Hubei Wuhan China School of Computer Science Wuhan University Hubei Wuhan China Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts Wuhan China

ISBN: (纸本)9798400712883

With the rapid development of artificial intelligence technology, its application in the field of education and teaching is becoming increasingly widespread, bringing revolutionary changes to traditional education models. To cultivate high-quality software vocational skills talents, a digital management platform based on AI+capability model is constructed to explore the innovative application of artificial intelligence in education and teaching, realizing intelligent auxiliary learning functions such as personalized resource recommendation, student growth monitoring, and achievement label portrait. At the same time, a series of intelligent teaching tools such as"AI+Code Generation, AI+Inspection Assistant, AI+Process Map" will be developed to explore and promote the vertical application of big models in talent cultivation. Through innovative models such as"introducing enterprises into education, cultivating students through real-life situations, AI+empowerment, teaching according to students’ aptitude, trinity, innovative training, strengthening processes, and diverse evaluations", it has been fully applied to the talent training scene of software technology field engineers, and its teaching effectiveness is significant. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of international Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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Optimization of Insurance Claim Cost Prediction Through Health Data and Machine learning 5

Optimization of Insurance Claim Cost Prediction Through Heal...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Mohan, Shivram Sharma, Sanidhya Agrawal, Sonali Kamatchi, S. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350356236

This research aims to estimate the health insurance claim costs using a machine learning algorithm with given potential health risks factors including age, sex, BMI, smoking status and city. To benchmark the prediction accuracy for various algorithms selected in this analysis, a health insurance dataset obtained from Kaggle is used, details of which are given as follows: It involves activities such as data pre-processing, feature engineering, model selection along with using measures like R-squared (R2) and Mean Absolute Error. The predictive accuracy results indicated that Random Forest rendered the best results among all the tested models with an accuracy of 96.7% in modeling the health data due to its rich ability to generate various relationships. This research also focuses on the capability of machine learning in cost forecasting for insurance companies that would improve on the pricing strategies among other aspects of financial management. © 2025 IEEE.

关键词： Health insurance

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