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Recyclable and Non-Recyclable Waste Recognition using Image Processing with Machine learning: Recycle Mate

Recyclable and Non-Recyclable Waste Recognition using Image ...

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intelligent Technologies (CONIT), international conference on

作者： Amit Nara Smit Panchal Prof. Suchita Dange Department of Computer Engineering A.P. Shah Institute of Technology Thane India

Waste Management is the effort needed to make our world a better place. As solid waste may cause a problem for our surroundings. Nowadays many people don't even know how to distinguish between recyclable and non-recyclable objects, we know only some general things which are recyclable like paper, plastic, cardboard and drinking cans. To deal with this problem a deep machine-learning Convolution Neural Network (CNN) was created to help us all with an achievable waste management task. The automated task works for everyone as it lets down the burden of work from many people’s shoulders. Our model is built using the Keras API. The model categorizes the data into a binary class. It has been tested with many objects which gave an acceptable prediction accuracy.

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Big data Analytics for Electrical Systems using Machine learning Algorithms

Big Data Analytics for Electrical Systems using Machine Lear...

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Technology, engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Salava V Satyanarayana Pillalamarri Madhavi Electrical and Electronics Engineering Hyderabad Institute of Technology and Management Hyderabad India

Big data Analytics plays a vital role in every electrical industry to analyze the large amount of data received from their dispatch centers. Typically, data retrieved from electrical systems like Generation, Transmission, and distribution will be analyzed using many control systems like SCADA and HMI without any human intervention. To analyze this data as per industry 4.0, Every system should be automated by integrating the data into the internet of things along with cyber security systems. With consideration of industry 4.0 standards, in view of effective optimized operational maintenance of electrical systems in the future, this paper provides intelligent predicted data by analyzing current and existing data that is fetching from electrical systems i.e., Various state electrical utility data files. The available data of electrical systems can be analyzed by using various Supervised Machine learning algorithms and this paper verified the accuracy of various machine learning algorithms by analyzing the predicted data by each algorithm.

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automated Helmet Detection and Fine Issuance System Using YOLOv8 & OpenCV

Automated Helmet Detection and Fine Issuance System Using YO...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Hirald Dwaraka Praveena Goruva Vardhan Babu Jinka Muni Sai Yateesh Degasikshi Guna Guduru Vamsi Krishna Dept. of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept of ECE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In densely populated countries like India, traffic violations are a major concern, especially among motorcyclists, leading to accidents and significant loss of life and property. A prevalent issue is the failure to wear helmets. To address this, a system called “Helmet Detection for Motorcyclists using YOLOv8 and OpenCV” is proposed, leveraging deep learning to automatically identify and penalize violators in real-time through traffic cameras. The system employs the You Only Look Once (YOLO) algorithm, a deep neural network-based object detection model, to identify riders without helmets. Subsequently, it detects the number plate of violators, extracting its text using the EasyOCR(Optical Character Recognition) library. This extracted data is cross-referenced with a pre-existing database to retrieve the owner's contact details. For automation, an SMS API(Application Programming Interface) is integrated to send fine notifications to the violator's registered mobile number. By encouraging helmet use, the system aims to minimize head injuries during accidents and improve road safety. This automated approach enhances traffic law enforcement, provides evidence of violations, and ensures efficient penalty issuance. Ultimately, the system supports safer roads by promoting compliance with traffic regulations and protecting motorcyclists' lives.

关键词： YOLO Deep learning Helmets Real-time systems Road safety Libraries data models data mining License plate recognition Injuries

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

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

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

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

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

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 models-DenseNet201, 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, AlexN et 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.

关键词： Deep learning Adaptation models Accuracy Sensitivity Malaria Computational modeling Microscopy Mortality Reliability Tuning

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automated Tooth Segmentation in X-ray Images using Attention Integrated U-Net++ Model 3

Automated Tooth Segmentation in X-ray Images using Attention...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Yaswanth, Julakanti Sai Kumar, Kanderi Johith Singh, Rimjhim Padam Bengaluru Amrita Vishwa Amrita School of Computing Dept. of Computer Science and Engineering Vidyapeetham India

ISBN: (纸本)9798331523893

Oral functioning, the ability to perform various oral functions, pain-free and without being ill, is one of the indicators of overall well-being and life satisfaction. Even though these diseases are mostly preventable, a majority of individuals all over the world suffer from periodontal disease, dental caries, and oral malignancies to an estimated of 5 billion people worldwide. However, there are still use of traditional radiographic techniques like periapical and panoramic radiography that assist in diagnosis. A better diagnosis is enhanced through the use of modern techniques in imaging such as MRI, ultrasonography and CBCT imaging. Deep learning-based solutions enhance diagnostic capabilities through dental segmentation in images since they learn from unmodified data in contrast to traditional machine learning and deep learning techniques which produce inferior results when dealing with massive datasets. Therefore, in an effort to solve the problem of efficient dental segmentation, anencoder-decoder basedtooth segmentation model based on U-Net++ architecture modified using Efficient Channel Attention Network is proposed that aims at enhancing the accuracy of dental diagnosis. The paper also focus of the performance of different state-of-art CNN techniques namely, U-Net, FCNs, ENet, U-Net++, U-Net 3+, SwiftNet on the standard Tuffs Dental dataset for model selection and comparisons. The proposed model outperforms other segmentation models and achieves the best mean average precision of 96.4% and a dice coefficient of 90%. © 2025 IEEE.

关键词： X ray radiography

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Detecting and Analyzing Threats for Network Traffic Control using Machine learning Approaches

Detecting and Analyzing Threats for Network Traffic Control ...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Richa Mathur Ritam Dutta Dept. of Computer Science & Engineering Poornima University Jaipur Rajasthan India

Today big data playing pivotal role in helping mankind with large amount of dataset that can be beyond human perception. Big data is ultra-fast and growing rapidly. This data is more valuable because it has hidden facts and insights after its process. But various challenges exist when we deal with such data with the help of traditional tools and technologies. Earlier the traditional network security solutions were insufficient to address different kinds of threats and network attacks. To resolve and restrict cyber threats and attacks is the most increasing concern in today's world of cyber security. With the advent of Machine learning (ML) technology, an automated and up-to-date security system has been provided through which we can detect, analyze and secure data for network traffic and cyber security. In this paper, a comprehensive analysis on various machine learning algorithms have been investigated to identify the glitches and provides intelligent data analysis report and automation in cybersecurity to extract valuable insights from cyber data.

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Upgraded Machine learning Techniques to Forecast Obesity Hazards

Upgraded Machine Learning Techniques to Forecast Obesity Haz...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sukanya N.S V. Asha Vasumathi M. T Sumit Chakraborty Sushanth S M Supriya Kumari Department of Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent years, investigators have prolonged their research on predicting obesity risks using Machine learning (ML) techniques. Obesity is an intricate condition with multiple contributing factors, such as genetics, behaviour, environment, and social factors. Predicting obesity risk through ML can aid recognise individuals who may be at risk also aids in early identification to prevent or manage the condition. There is a need for these predictive models which is a combination of various factors like age, medical history, and lifestyle and these are the probability of increasing obesity among individuals. The model aims to accurately predict the obesity risks as early as possible. Detecting obesity at an initial stage is a stimulating procedure. This paper aims to use ML models such as XGBoost, CatBoost and LightGBM to predict obesity at a primary stage. To predict the obesity, Nobeyesdad dataset from Kaggle is used in this study. The obesity prediction model classifies the data into multiple categories like normal, insufficient weight, overweight and obesity individuals. The results of the enhanced obesity prediction model generates ideal results by classifying the datasets which aids the specialists to make decisions easily.

关键词： Training Obesity Machine learning algorithms Machine learning Predictive models Prediction algorithms data models Social factors Classification algorithms Internet of Things

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R2I: A Deep Fault Diagnosis Semantic Segmentation System Based on Knowledge Distillation in Photovoltaic Panels

R2I: A Deep Fault Diagnosis Semantic Segmentation System Bas...

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Energy, Power and Electrical engineering (EPEE), international conference on

作者： Xiaoyanghao Lei Kai Ma Junyi Zhao Department of Electrical and Electronic Engineering University of Liverpool Liverpool United Kingdom Department of Computer Science Xi'an Jiaotong-liverpool University Suzhou China

With the growing adoption of photovoltaic power systems, ensuring the reliable and efficient operation of photovoltaic panels is critical. However, defects such as cracks, dirt, and failures are common during long-term use. Manual inspection is subjective and resource intensive. Recent computer vision and machine learning techniques present new opportunities for automated diagnosis. This paper reviews photovoltaic panel defect detection using deep learning. Two main approaches exist: visible light and infrared imaging. Earlier works employed object detection with lower precision. Newer methods use semantic segmentation for pixel-level accuracy. However, most focus on single modalities and datasets. Transfer learning across diverse data could improve generalization. Few works estimate defect areas, needed for automated analysis. This paper proposes a dual-modality semantic segmentation approach with knowledge transfer learning. It shares representations across visible and infrared datasets via distillation. A lightweight network reduces parameters and computations for deployment. Soft targets provide latent features transferable to new data. An algorithm estimates defect area proportions to assess hazard levels. The multi-modal transferable learning system aims to advance automated, accurate photovoltaic panel defect diagnosis. Key innovations include tackling insufficient data via shared knowledge, reducing model complexity, improving generalization, and pioneering defect severity assessment. This can facilitate intelligent photovoltaic inspection to ensure system health, safety and longevity.

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Transforming Rice Disease Detection: VGG16 CNN for automated Leaf Disease Classification

Transforming Rice Disease Detection: VGG16 CNN for Automated...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Md Ankushavali Jatin Sharma School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

about half of the world's population depends on rice as a basic food; but many leaf diseases-including Tungro, Brown Spot, Blast, and Bacterial Blight-threatens its output. Early and accurate diagnosis of these diseases is essential for good crop management since often-manual inspection methods are slow and prone to error. This work uses the VGG16 convolutional neural network (CNN) model to examine automatic categorization of rice leaf diseases. Created from the dataset were training, validation, and testing sets to assess model performance. After training, the model showed remarkable overall accuracy 95% great precision, recall, and F1-scores over all disease categories. The VGG16 model seems to be a useful instrument for improving control and detection of rice leaf disease. Quick diagnosis of disease symptoms allows this automated approach to significantly improve crop management strategies, reduce farmer economic losses, and therefore guarantee food security. The study highlights the opportunities of deep learning technology in agricultural applications, therefore providing the route for more continuous and efficient disease detection methods in rice farming.

关键词： Training Deep learning Accuracy Microorganisms Crops Food security Inspection Feature extraction Convolutional neural networks Diseases

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automated Fish Species Classification Using Convolutional Neural Networks

Automated Fish Species Classification Using Convolutional Ne...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

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

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

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.

关键词： Adaptation models Accuracy Biological system modeling Fisheries data augmentation data models Convolutional neural networks Biodiversity Reliability Monitoring

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