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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Badaya, Harsh Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Vellore Institute of Technology Computer Science and Engg Tamil Nadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

Lung cancer is one of the most deadly and persistent diseases in the world today, making early identification of this disease crucial. This research focuses on the integration of cutting-edge deep learning technologies, namely Convolutional Neural Networks (CNNs), with modern medical insights to improve lung tumour detection using plain chest X-rays (CXR). Using a novel technique to transfer learning, we optimise our model's effectiveness for better diagnostic results in the early identification of lung cancer. Our suggested model, which combines ResNet50, EfficientNetB0, and DenseNet121, achieves outstanding accuracy (97.41%) while minimising loss (0.0803), exceeding individual models. This unique model combines the characteristics of many architectures to provide improved feature extraction and sophisticated pattern identification in medical imaging. Beyond a literature review, our study adds a real answer that has the potential to revolutionise early detection, giving a powerful weapon for combatting this lethal illness. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law 17

Annotators-in-the-loop: Testing a Novel Annotation Procedure...

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17th Linguistic Annotation Workshop, LAW 2023, LAW 2023, co-located with ACL 2023

作者： Zanoli, Emma Barbini, Matilde Riva, Davide Picascia, Sergio Furiosi, Emanuela D’Ancona, Stefano Chesi, Cristiano IUSS Pavia School for Advanced Studies NETS Lab. Italy Università degli Studi di Milano Department of Computer Science Italy IUSS Pavia School for Advanced Studies Italy

ISBN: (纸本)9781959429838

The availability of annotated legal corpora is crucial for a number of tasks, such as legal search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Advances and Innovation in Digital Technologies

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Recent Patents on Engineering 2022年第2期16卷 1-2页

作者： Tanwar, Poonam Madaan, Rosy Computer Science & Engineering Department Manav Rachna International Institute of Research & Studies Haryana Faridabad121004 India

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The Laplacian Energy of Hesitancy Fuzzy Graphs in Decision-Making Problems

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computer Systems science & Engineering 2023年第3期44卷 2637-2653页

作者： N.Rajagopal Reddy Mohammad Zubair Khan S.Sharief Basha Abdulrahman Alahmadi Ahmed H.Alahmadi Chiranji Lal Chowdhary School of Advanced Sciences Vellore Institute of TechnologyVellore632014TamilnaduIndia Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia Department of Mathematics School of Advanced SciencesVellore Institute of TechnologyVellore632014TamilnaduIndia School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the *** for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM *** objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy ***:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in ***:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth *** aim to identify the best one from the different testing *** are alsofinding the best operation schemes in the *** this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.

关键词： Hesitancy fuzzy graphs(HFGs) laplacian energy satellite communication system reservoir operation schemes decision-making

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Classification Medical Data Analysis Medical Imaging

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An AI-integrated Enhanced Anatomical Brain Tumor Segmentation in Substantia Nigra for Early Disease Identification using Deep Resnet50-CNN

An AI-integrated Enhanced Anatomical Brain Tumor Segmentatio...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： P. V. Ganeshkumar S. Prasanna Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Application Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification.

关键词： Neuroimaging Image segmentation Accuracy Magnetic resonance imaging Brain tumors Feature extraction Pattern recognition Particle swarm optimization Tumors Diseases

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Cervical Disc Herniation Detection Using Morphological Image Processing and Neural Network Classification

Cervical Disc Herniation Detection Using Morphological Image...

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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： C. Malarvizhi A. Muruganantham Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Science (PG) Kristu Jayanti College Bengaluru India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

Intervertebral discs, the rubbery cushions between spinal bones, can bulge or rupture (herniate) beyond their normal boundaries. This can irritate nearby nerves, causing pain, particularly in the upper back with cervical disc herniation. This work explores the use of morphological transformations of image and simulated neural network classification to analyze magnetic resonance imaging (MRI) data for automatic detection of disc herniation. Morphological operations manipulate individual pixels in an image based on the measurement of their surrounding pixels. In this paper, these techniques are employed for herniated regions identified in the magnetic resonance image scans. Specifically, the edge-detected canny image is suitable for identifying the edges of these regions, followed by morphological operations with structuring elements to refine and enhance the image quality. Using non-flat structuring elements such as arbitrary and ball applied to the images. Morphological operations named dilation and erosion are applied for four stages of herniated canny images. A neural network classification method is employed to analyze the processed images at different stages of disc herniation. This allows for the calculation of the effectiveness of each structuring element in accurately identifying herniated discs. The results of the morphological operations could be used to extract quantitative measurements of hernia size, shape, or other relevant features, aiding in treatment planning and monitoring.

关键词： Shape Magnetic resonance imaging Image edge detection Shape measurement Neural networks Feature extraction Size measurement

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Cloud-Driven Wearables using Naive Bayes Algorithm and advanced Sensors in Smart Fabrics 3

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Praveena, M.D. Anto Gupta, Nukala Sujata Marin, V. Bini Jeslin, J Gnana Meenakshi, R. Muthulekshmi, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world. © 2024 IEEE.

关键词： Smart textiles

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