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Placement Prediction Using the Artificial Neural Network (ANN) 3

Placement Prediction Using the Artificial Neural Network (AN...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Priyadarsini, Jukta Badiger, Veena S. Sheetal Jacintha, Anitha DSouza Presidency College Department of Computer Applications Bengaluru India Presidency University Presidency College School of Engineering Bengaluru India

ISBN: (纸本)9798331515911

Placements are of utmost significance to academic organizations and college students. A strong foundation for the professional field is built up for the student beforehand, and a positive placement report gives a school or college a competitive edge in the educational marketplace. An automated method of statistical evaluation that streamlines the creation of analytical versions is machine learning. In this study, a computer that predicts whether a student might be located or not is the focus, based solely on the student's credentials, historical data, and experience. The performance is evaluated using students' datasets from presidency college and accuracy achieved for our model is 96%. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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Dragon Fruit Maturity Detection and Quality Grading with Scalable Deep Neural Networks 3

Dragon Fruit Maturity Detection and Quality Grading with Sca...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Aishwarya, N. Devisowjanya, P. Shah, Deshana Vikas Niharika, Raavi Department of ECE Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331527549

The paper addresses the critical need for an automated system to accurately determine the ideal harvest time for dragon fruit. It effectively differentiates between fresh and defective fruit, as well as mature and immature fruit, to maintain high-quality standards. The study presents an advanced approach for detecting the maturity and freshness of dragon fruit using pre-trained deep learning models, including VGG16 (a lightweight model), MobileNetV3 (a moderate-weight model), and EfficientNetB0 (a heavy-weight model). Each model utilizes three different activation functions: Exponential Linear Unit (ELU), Rectified Linear Unit (ReLU), and Scaled Exponential Linear Unit (SELU). The features extracted by these models, using the best activation function, were then employed to train five different machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Random Forest Classifier, AdaBoost, and CatBoost. Experimental results indicate that for maturity detection, the combination of EfficientNetB0 with the SVM Classifier yields the most favorable results. For quality grading, the optimal outcomes are achieved with the MobileNetV3-SVM combination. This hybrid approach achieves a precision and recall of 99%, along with 99% accuracy for both maturity and freshness detection, thus facilitating the assessment of dragon fruit. © 2025 IEEE.

关键词： Support vector machines

来源：评论

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An Attention-based Deep learning Model for Jasminum Quality Grading 3

An Attention-based Deep Learning Model for Jasminum Quality ...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Karthikeyan, P. Uma Maheswari, P. Shivani Suvatheka, S. Pradeep Kumar, G. Al Mohamed Bilal, A. Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Jasmine flowers, which are much cultivated in the states of Tamil Nadu and Karnataka, contribute more than ?20 crores annually to the Indian economy through internal sales and exports. Traditional grading practices, however, suffer from human subjectivity, often resulting in inconsistencies. This paper presents a deep-learning-based grading system that utilizes the InceptionResNetV2 model with self-attention mechanisms to classify flowers based on bud tip size, freshness, color, and tubule presence. The system focuses on fine-grained details rather than the whole flower and achieves 92% accuracy, which ensures consistency and fairness in quality assessment. The automated approach is significantly more efficient, transparent, and scalable and likely to be compatible with global trade standards of jasmine flowers. © 2025 IEEE.

关键词： Adversarial machine learning

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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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AI-based automated Subtitle Generation System for Multilingual Video Transcription and Embedding 3

AI-based Automated Subtitle Generation System for Multilingu...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Penyameen, K. Siva Suriya Rajan, G.M. Arshath Ahamed, A. Yugesh Ram, S. John Shiny, J. Periya Nayaki, A. Department of Information Technology Velammal College of Engineering and Technology Tamilnadu Madurai India

ISBN: (纸本)9798331527549

The subtitle generator system proposed in this project is an intelligent tool towards automation of subtitle production for video content, using the most advanced speech recognition and transcription and video processing technologies. Using integration with machine learning models such as Whisper by OpenAI with multimedia tools including FFmpeg and MoviePy, it manages the production of subtitles that track up and are perfectly synchronized with spoken content within video files. It gives standard SRT file generation with direct embedding abilities into videos, hence reducing the total manual effort and time taken in the production of subtitles. Support to transcribe into multiple languages is also given with an easy Graphical User Interface GUI that provides ease of interaction with the application. This solution to video accessibility, particularly for the purpose of using more than one language, is scalable and automated to provide the best possible approach in media industry subtitle generation. © 2025 IEEE.

关键词： Graphical user interfaces

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Plant Leaf Disease Identification based on Machine and Deep learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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automated Fish Species Classification using Convolutional Neural Networks 3

Automated Fish Species Classification using Convolutional Ne...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yamsani, Nagendar Nasra, Parul Gupta, Sheifali School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Appropriate classification of species in fish is important for biodiversity conservation, management of fisheries, and ecological research. This paper introduces a novel deep learning method for the automatic classification of aquatic species using CNNs. Testing the proposed model on a large-scale and diverse fish dataset resulted in impressive classification accuracy, reaching 92%. The architecture is made to balance the computational cost and predictability. It uses more complicated techniques, such as data augmentation, batch normalization, and dropout, in order to generalize. The study demonstrated evaluation of the model on multiple categories of the fish species and showed that it is precise and reliable in classification. The results show a potential for using CNN-based models for fully automating fish classification tasks, providing a scalable tool with a high degree of reliability in monitoring marine and freshwater ecosystems. Future work includes incorporating real-time detection capabilities into the model and adapting the same design to classify species in video streams. © 2025 IEEE.

关键词： Invertebrates

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data-Driven Approaches for Personalized Sleep Disorder Diagnosis 3

Data-Driven Approaches for Personalized Sleep Disorder Diagn...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Asmita, R. Hrithik, T.H. Akhil, K.H. Mishra, Nisha Kirthika Devi, V.S. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

These days, sleeping disorders are a concern for young people and people with a variety of health issues. It is important to keep in mind that sleep problems are not a recent occurrence and should not be sought after or accepted as a trend, even though it is true that issues related to sleep have garnered more attention and discussion in recent years due to growing knowledge and research. Numerous problems, such as anxiety, depression, and mental health strain, can be brought on by this illness. Machine learning (ML) can handle a wide range of tasks, including classification, prediction, and decision-making. To analyze and identify sleeping disorders using ML and can also be employed to gauge a person’s level of stress. This paper presents the DL models and ML algorithms that have been deployed to sleep stage classification in the literature to date. The effectiveness of Adaboost models is investigated in this work. These models’ research findings suggest the ideal parameter combination for improved performance. © 2025 IEEE.

关键词： Sleep research

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An Efficient Deep learning based Approach for Pneumonia Detection 3

An Efficient Deep Learning based Approach for Pneumonia Dete...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shirisha, M. Sri Sai Durga, M. Bhanu Keerthan Reddy, G. Shanmugasundaram, Hariharan Vaishnavi, Bandar Natarajan, Karuppiah Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9798331527549

Pneumonia is a major global cause of death, hence early detection and precise diagnosis are essential for successful management. Conventional techniques for detecting pneumonia, like radiological interpretation and clinical assessment, are frequently laborious and prone to variation amongst observers. Deep learning models have transformed medical imaging by offering automated, reliable, and accurate diagnostic instruments. The goal of this paper is to use data (image) pre-processing techniques to increase the accuracy of the deep learning model, EfficientNetB0 for pneumonia identification. The dataset utilized in this paper consists of annotated chest X-ray images from the publically accessible source "Chest X-Ray Images (Pneumonia)";Paul Mooney submitted 5,863 photos in 2 categories on Kaggle. To enhance the model’s resilience and ability to generalize, the images undergo preprocessing using techniques such as normalization, gray scaling, median filtering, CLAHE, and data augmentation. A standardized evaluation framework is used to evaluate each model’s performance, and measures like F1 score and confusion matrix analysis are used. The results of this paper demonstrate how pre-processing the data can increase the accuracy of the deep learning model and can enhance medical imaging diagnostic accuracy and dependability. © 2025 IEEE.

关键词： Median filters

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Deep learning-based Malaria Detection: A Comparative Study of Algorithm Performance 3

Deep Learning-based Malaria Detection: A Comparative Study o...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saran, K. Vijay, M. Hariharan, M. Prema, D. Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Malaria is caused by Plasmodium parasites and remains a serious threat to health worldwide, with high morbidity and mortality rates. However, effective treatment depends on the accurate and fast diagnosis of malaria, which traditionally has been through microscopy and rapid diagnostic tests that have limitations, including requiring expert interpretation, limited sensitivity in the early stages of infection, and variable accuracy. By automatically detecting malaria from microscopic blood smear images using deep learning models, our study has overcome the above constraints. Four sophisticated deep learning modelsDenseNet201, NasNet, EfficientNetB7, and AlexNet were tested against performance metrics of accuracy, precision, recall, F1 score, and computational efficiency. With 99% accuracy, NasNet was the most successful model among them and is hence very dependable for the diagnosis of clinical malaria. In addition, AlexNet was faster in inference speed and had moderate accuracy compared to DenseNet201 and EfficientNetB7. Such results are a significant step forward in malaria detection technology, and the potential of NasNet for automated, scalable diagnostic systems is promising. © 2025 IEEE.

关键词： Contrastive learning

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