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Advancements in AI-Powered Robotic Cleaning Systems: Autonomous Path Planning, Predictive Maintenance, and Cleanliness Assessment Frameworks 3

Advancements in AI-Powered Robotic Cleaning Systems: Autonom...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jones Sarah, A. Nandhakumar, D. Kumar, Atchitha Magesh Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Artificial Intelligence (AI) is transforming robotic cleaning systems to be more efficient, safe, and adapt in various environments, including public spaces and industrial facilities. The novelty of these systems is the use of advanced sensing and deep learning to identify areas that need cleaning, plan paths, and save resources all while meeting high cleanliness standards required in sensitive areas like hospitals and pharmaceutical facilities. The objective is how recent developments in path planning, dirt detection, and predictive maintenance improve these systems, which reduce costs and perform without human supervision. Though these systems work well, challenges remain in deciding on cleaning paths for complex spaces, correctly detecting various types of dirt, adapting to changing environments, and maintaining to prevent breakdowns. © 2025 IEEE.

关键词： Predictive maintenance

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Effectiveness of Class Imbalance Treatment for Diagnosis of PCOS Using Machine Learning Models: A Comparative Study 10

Effectiveness of Class Imbalance Treatment for Diagnosis of ...

引用

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

作者： Majumdar, Sounak Biswas, Saroj Kr. Purkayastha, Biswajit National Institute of Technology Silchar Department of Computer Science and Engineering Silchar India

ISBN: (纸本)9798350384369

Polycystic Ovary Syndrome (PCOS) is the most prevalent hormonal disorder among women during their reproductive years. It is one of the leading causes of female infertility. Diagnosing peos is difficult because the symptoms of PCOS vary widely and there is no single medical test that can accurately diagnose PCOS. This encourages researchers to develop data driven models for PCOS diagnosis. However, a very common challenge in the data driven approach for medical diagnosis is the problem of imbalanced dataset. This paper presents a comparative study on the effectiveness of different oversampling techniques in handling the class imbalance problem for PCOS diagnosis. This study has explored six oversampling techniques in combination with thirteen different machine learning models which includes eight single classifier-based models and five ensemble models. The dataset used for this study is available on Kaggle machine learning repository. Experimental results shows the effectiveness of class imbalance treatment for PCOS diagnosis. Highest accuracy of 92.9% has been achieved by the model that uses Gaussian SMOTE for oversampling and Extra Trees for classification. © 2024 IEEE.

关键词： Contrastive Learning

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Lung and Colon Cancer Detection using Deep Learning Techniques 3

Lung and Colon Cancer Detection using Deep Learning Techniqu...

引用

3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Sharma, Deepali Choubey, Dilip Kumar Thakur, Kavish Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bihar India

Lung and colon cancer are among the most frequent cancers that claim lives around the globe. For people to heal, early detection and assistance are crucial. Physicians diagnose patients using histopathological images of tissue biopsies from potentially infected areas of the colon and lung. Most types of lung and colon cancer are challenging to diagnose and often require numerous tests. The article presents a novel approach to identify pulmonary and colorectal tumors using deep learning algorithms on histopathology images. Our objective is to develop a dependable and efficient system that uses pre-trained CNNs with LC25000 histopathological image datasets to accurately identify malignant tissues. This work promotes medical examination and artificial intelligence by resolving significant challenges in cancer detection and improving the accuracy and effectiveness of pathological assessment. The implemented deep learning algorithms are: Vgg16 with random forest classifier, Vgg16 with Xgboost classifier, and GLCM with LightGBM. The results of Vgg16 with the Xgboost classifier technique showed better performance, with 97.46% accuracy. In the future, we can use image segmentation to conduct research on nuclei detection and cell segmentation. We can utilize GAN, which significantly enhances the accuracy of medical image segmentation due to its strong generation capabilities and ability to capture data distribution. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep learning

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A Deep Learning Approach With Sparse Autoencoder for Alzheimers Disease Classification 23th

A Deep Learning Approach With Sparse Autoencoder for Alzheim...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Oommen, Deepthi K. Arunnehru, J. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Tamilnadu Chennai India

ISBN: (纸本)9783031648120

Alzheimer's Disease is a progressive neurodegenerative condition distinguished by a steady deterioration in cognitive abilities. Given the empirical inquiries conducted, it is imperative to take age into account as a fundamental criterion when selecting participants. Younger individuals are more vulnerable to the transient aspect compared to those who are older. The current work focused on selecting young-onset subjects, as early diagnosis can significantly impact patients' lives. The identification of this disease at an early stage using traditional means poses significant difficulties. Deep Learning (DL) has emerged as a highly effective approach for enhancing the performance of diagnostic processes and boosting forecast accuracy. The research utilized deep learning techniques and neuroimaging approaches to autonomously identify and categorize the condition based on its phases of mild cognitive impairment. The suggested study is conducted through a series of three sequential phases. Prior to further analysis, the 3D input visuals need to undergo image preprocessing with Weiner filtering in order to remove noise and smooth the image. Following, Transfer learning models are utilized to extract features, which are then compressed through the use of cascaded Auto Encoders. The ultimate stage involves the utilization of a fine tuned Deep Neural Network (DNN) for categorizing of the phases of AD into five classes. The combination of the ResNet-18 and sparse autoencoder with the deep neural network model yielded a remarkable accuracy rate of 95.62%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Enhancing Bakery Industry Supply Chain Management Through Blockchain 5th

Enhancing Bakery Industry Supply Chain Management Through Bl...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Raj Pal Verma, Rajat Department of Computer Science and Engineering Pranveer Singh Institute of Technology UP Kanpur India

ISBN: (纸本)9789819771776

In Supply Chain, the Blockchain technology is important in the transformation of the Bakery Industry. The Blockchain technology is instrumental in ensuring product authenticity, transparency, and product traceability. This paper focuses on a Blockchain-Based technology that is envisioned to transform the Supply Chain Management (SCM) in the baking industry. In proving quality control and confidence in the food business, other factors considered in the design are interoperability and ongoing distribution of blockchain technology. By integrating blockchain technology and smart contract, the design creates a network that is safe and visible that follows up the bakery foods flow from raw items to the final consumer. The SCM system design anchors the Blockchain technology in each production stage, packaging, consignment, and distribution. Such an approach would ensure accountability and reduce the risk of fraud or fake products since the parties would have access to up-to-date, unchangeable information. In addition, application of Blockchain technology would increase the likelihood of traceability, aiding in the timely detection and resolution of any quality or safety issues. Thus, the partners would be better aware of the sources of ingredients and the processes of production and product handling, which would help win the clients’ trust and meet the regulations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bakeries

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Hyperspectral Image Classification Using Quantum Machine Learning 1

Hyperspectral Image Classification Using Quantum Machine Lea...

引用

1st International Conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Priyanka, G.S. Venkatesan, M. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367720

Comprehensive spectrum information is provided by hyperspectral imaging and is necessary for data processing and remote sensing. Yet, efficient categorization is hampered by its great dimensionality and intricate spectral fingerprints. We present a novel method in this paper to tackle these issues by utilizing Quantum Machine Learning (QML), more especially Quantum Neural Networks (QNN). In order to maintain crucial spectral information while lowering data complexity, we use dimensionality reduction techniques like Principal Component Analysis (PCA) and Kernel PCA (KPCA). To develop Modified Quantum Neural Network (MQNN) models for multi-class classification, these reduced representations are coupled with conventional Neural Networks (NN). Tested on the Salinas, Pavia, and Indian Pines datasets, our findings show that QML approaches are more accurate when processing high-dimensional hyperspectral data, presenting new opportunities for use in environmental monitoring, agriculture, and urban planning. © 2024 IEEE.

关键词： Dimensionality reduction

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CSM2Net: Exploring Effective Fusion of Complex Spectral Masking and Complex Spectral Mapping for Speech Enhancement 7

CSM2Net: Exploring Effective Fusion of Complex Spectral Mask...

引用

7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Lu, Yun Kausar, Tasleem Wang, Xiaoye School of Computer Science and Engineering Huizhou University Huizhou China Mirpur Institute of Technology Mirpur University of Science and Technology Mirpur Pakistan

ISBN: (纸本)9798350350890

Speech enhancement is essential to front-end speech signal processing, which can effectively improve speech quality and clarity. Complex spectral masking and complex spectral mapping are the main methods for speech enhancement in the time-frequency domain. This paper proposes a novel fusing complex spectral masking and complex spectral mapping networks (CSM2Net) to combine both advantages. The complex spectral masking and spectral mapping branches share the same encoder and interact in the decoding stage through a dual-branch cross-attention block. In the encoding and decoding stage, a novel soft-threshold attention block extracts speech time-frequency features and makes the model focus on more critical features. Finally, the outputs of the two branches are fused by a weight-generating block, which efficiently selects the better spectrum. On the test set, CSM2Net achieves advanced performance using almost minimal model parameters compared to other state-of-the-art models. © 2024 IEEE.

关键词： Decoding

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An Efficient 3-D Model for Early Prediction of Breast Cancer Based on Hybrid ANN - Fuzzy Model through Lossless Medical MR Images 5

An Efficient 3-D Model for Early Prediction of Breast Cancer...

引用

5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Renukadevi, P. Lakkshmanan, Ajanthaa Brindha, R. Jeyakumar, D. Srm Institute of Science and Technology Dept of Computing Technologies Kattankalathur India Dhanalakshmi College of Engineering Dept of Computer Science and Engg Chennai India

ISBN: (纸本)9798350321425

Rapid advancements in technology has aided in early prediction of Breast cancer which is a high mortality rate characterized condition. Fuzzy and Neural network-based models have been effective in prediction of early occurrences of breast cancer. Any method now in use must be able to detect breast cancer in women at an early stage to effectively bring down the mortality rate. The major goal of the suggested approach is to apply 3-D picture lossless compression in NN to determine the tissue size from an MRI breast scanning image. The use of a 3-D predictor to categorise the photos and the application of the NN approach to determine the degree of breast cancer accuracy are the primary features of this work. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Enhancing Power Distribution Protection: A Comprehensive Analysis of Renewable Energy Integration Challenges and Mitigation Strategies 14

Enhancing Power Distribution Protection: A Comprehensive Ana...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Alves, Everton Reiz, Cleberton Melim, André Gouveia, Clara Institute for Systems and Computer Engineering Technology and Science - INESC TEC Porto Portugal

ISBN: (纸本)9781837242689

The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of Engineering & technology 2024.

关键词： Power distribution networks

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms 1

Smart Agriculture Inventory Management System Using Machine ...

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1st International Conference on computing, sciences and Communications, ICCSC 2024

作者： Kanyal, Hoshiyar Singh Jain, Harshit Tyagi, Harsh Yadav, Satyarth Prakash ABES Institute of Technology Ghaziabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350353648

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices. © 2024 IEEE.

关键词： Agricultural economics

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