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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Early Detection of Colorectal Cancer from Polyps Images Using Deep Learning 7th

Early Detection of Colorectal Cancer from Polyps Images Usin...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Kumar, Ravi Singh, Amritpal Khamparia, Aditya Department of Computer Science Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science Engineering Jawaharlal Nehru Government Engineering College Himachal Pradesh Sundernagar Mandi India Department of Computer Science Babasaheb Bhimrao Ambedkar University Amethi India

ISBN: (纸本)9789819720309

Detecting colorectal cancer in early stages and predicting associated risk is very important as it has the highest mortality rate after lung cancer. The polyps present in the colon and rectum are one of the main causes of colorectal cancer. In this study, we propose different deep learning methods to classify images as normal or containing polyps. The performance of these models was evaluated via implementation on public and private datasets. On the WCE dataset, the OEM model implemented with transfer learning achieved a classification accuracy of 99.5%, 96.37%, and 96% on training, validation, and test set, respectively. On the Kvasir dataset, the OEM model obtained a classification accuracy of 97.80%, 96.40%, and 96.03% on training, validation, and test set, respectively. On SCPolyps dataset, the OEM model achieved a classification accuracy of 93.80%. Hence, the utilization of deep learning techniques can be viewed as an efficient approach to creating tools for assisting in the diagnosis of CRC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Machine Utilization Prediction in Cloud Using Informer Model 4

Machine Utilization Prediction in Cloud Using Informer Model

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Naik, Archana Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. By leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LSTM, Prophet, and XGBoost across diverse CPU utilization datasets. Performance is evaluated using RMSE, MSE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. Specifically, the Informer achieved a 47.51% reduction in RMSE over GRU, 49.90% over LSTM, 67.36% over Prophet, and 50.46% over XGBoost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation. © 2024 IEEE.

关键词： Prediction models

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Identifying Chinese Handwriting Characteristics for Detecting Children with Autism 24

Identifying Chinese Handwriting Characteristics for Detectin...

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39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Yen, Ling Kai Wong, Jasin Chen, Arbee Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (纸本)9798400702433

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, as well as restricted and repetitive behaviors. ASD affects individuals across various aspects of their lives, including their cognitive, social, and motor skills. Early detection and intervention are crucial for improving outcomes and providing appropriate support to individuals with ASD. Handwriting is an essential skill that children develop early in life, and it provides valuable insights into their motor control, coordination, and cognitive processes. Good handwriting is crucial for academic progress, social and communicative development, and development of self-esteem. By identifying the handwriting characteristics exhibited by ASD children, we aim to develop a reliable and objective method for detecting ASD. We aim to identify the handwriting characteristics associated with ASD using deep learning techniques. The characteristics of Chinese characters, such as straight lines and turns, are considered in analyzing the writing characteristics of ASD children. We present a dataset of ASD children's handwriting in Traditional Chinese and fine-tunes three models to build a classification model. Experiment results demonstrate the model's effectiveness in distinguishing between the handwriting of ASD children and typically developing children, achieving F1 score of 93.6%. The study provides valuable insights into handwriting characteristics in ASD and suggests directions for future research in this area. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diseases

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Advancement in EEG Signal Feature Extraction for Enhanced Seizure Detection and Prediction 8

Advancement in EEG Signal Feature Extraction for Enhanced Se...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Khattar, Sonam Kaur, Ravinder Chandigarh University Department of Computer Science Mohali India Thapar Institute of Engineering and Technology Department of Computer Science Punjab Patiala140413 India

ISBN: (纸本)9798331505462

Epileptic seizure is a highly severe neurological disorder that abruptly interrupts the daily life of those afflicted. In order to advance the technology utilized for managing epileptic seizures, new diagnostic procedures have concentrated on creating electroencephalogram (EEG) techniques that are based on machine learning and deep learning models. The main aim of this research is to focus on some of thecommonly used methods for feature extraction as mentioned in the literature. It will also delve into potential uses of these methods. There are many ways described in this article to process and extract features from EEG signals. Those include time, frequency, decomposing, time-frequency and spatial. Finally, we look at different areas where artificial intelligence is utilized including assistive technology, classification of brain related diseases brain-computer interface systems and integration of machine learning techniques. These applications contribute to a general architecture for analyzing EEG signals. Lastly, potential directions in EEG signal analysis involving feature extraction will be discussed. This study's findings will guide researchers find out which machine learning models that are most effective when it comes to deep learning as well as how feature extraction can be done so that it can enhance detection accuracy of epilepsy seizure based on EEG data. © 2024 IEEE.

关键词： Frequency domain analysis

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A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

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computers, Materials & Continua 2024年第8期80卷 1831-1850页

作者： Hina Gul Sana Ullah Ki-Il Kim Farman Ali Department of Computer and Software Technology University of SwatMingora19130Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Applied AI Sungkyunkwan UniversitySeoul03063Republic of Korea

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart *** and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of *** paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such *** proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual *** protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base *** performance of the proposed protocol is evaluated through the network simulator *** different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network *** 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

关键词： Internet of Things wireless sensor networks traffic load clustering routing energy efficiency

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A Cross Design for Breast Cancer Prediction 1

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Saini, Hemant Kumar Preeti Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Chandigarh University Punjab Gharuan India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

Breast cancer in women’s becoming the serious cause moving to the morbidity and the mortality worldwide. This paper aims to design the hybrid model using various machine learning classification algorithms like k-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), and Gaussian Naïve Bayes to predict the breast cancer. Moreover, the accuracy has been increased with the varied classifiers and decides the precision using the f1- Score, and Jaccard index. The data set is of Kaggle is used to train the model and then test them using the machine learning algorithms. The proposed model is also prepared forum foreseen data in future by validating using K-fold Cross Validation Technique. By Random Forest Classifier (RFC) model exhibit the significant performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Real-Time Hate Speech Recognition Along with Educational Feedback and Automatic Reporting 4

Real-Time Hate Speech Recognition Along with Educational Fee...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Panda, Abhilasha Anand, Abhijeet Bebortta, Sujit Sekhar Tripathy, Subhranshu Mukherjee, Tanmay University Department of Computer Science and Engineering Bhubaneswar751030 India Ravenshaw University Department of Computer Science Cuttack753003 India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar751024 India

ISBN: (纸本)9798331528171

In today's digital era, the rise of toxicity, discrimination, and harm on online communication platforms has become a significant concern, necessitating effective moderation strategies. Existing methods for hate speech detection often lack real-time capabilities and fail to provide constructive feedback to users. Natural Language Processing (NLP) is used to identify the intent of the speech and bidirectional Long Short Term Memory (LSTM) machine learning algorithm classifies text into multiple hate speech categories. The designed system also displays charts, providing a detailed assessment and understanding of the nature and severity of the content. With a user-friendly interface, users receive real-time educational feedback on the nature and impact of different types of hate speech, promoting awareness and discouraging toxic behavior. © 2024 IEEE.

关键词： Speech recognition

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ML Shield: Detecting Malicious Websites with Machine Learning 15

ML Shield: Detecting Malicious Websites with Machine Learnin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tripathi, Aditya Shankar Maurya, Abhishek Jain, Aniket Kumari, Anjali Singh, Manu Department of Computer Science ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331300579

Phishing websites have become a significant cybersecurity threat, hosting malware and exploiting users by mimicking popular sites. Victims suffer financial loss, compromised privacy, and damaged reputation. Urgent solutions are needed to mitigate these threats promptly. Our goal is to develop a Chrome extension that utilizes machine learning and deep learning techniques to identify phishing websites and alert users when they visit such sites. When a user opens a website in their browser, the extension operates in the background. If the website is recognized as phishing, a pop-up box with an 'OK' button informs the user. If the website is not flagged as phishing, no action is taken. We employed machine learning algorithms (such as Random Forest) and a Single Layer Perceptron for training the model. The browser extension is written in JavaScript, and its JavaScript file extracts URL features like browser popups, use of frames, and shortening services. The algorithm then determines whether the website is legitimate or not. © Grenze Scientific Society, 2024.

关键词： Phishing

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Denoising Ancient Epigraphical Estampages from India using Transfer Learning with Surrogate Benchmarking 15

Denoising Ancient Epigraphical Estampages from India using T...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhruva, G. Parthasarthy, Srikrshna Koti, Anirudh Avabratha, Varun Vishwanatha Preethi, P. Pes University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Archaeological inscriptions in India are of immense historical importance offering conclusive evidence about the reign of various dynasties. Several obsolete languages, such as the Brahmi script, have survived as inscriptions on copper plates, slabs, pillars & other artifacts. Many ancient epigraphical carvings have deteriorated due to insidious exposure to natural elements, decay, deliberate destruction, and a multitude of other factors. To facilitate computational transcription, this paper proposes the use of transfer learning methods with models such as Autoencoder and U-Net for effectively denoising degraded epigraphical estampages, supplemented by a structured image processing framework for downstream character recognition. In the absence of definitive ground truth, we rely on surrogate benchmarking to quantitatively evaluate the effectiveness of the models on alternative datasets, particularly in the context of epigraphical denoising. The proposed U-Net Model is benchmarked on the publicly available SIDD denoising dataset, obtaining an SSIM score of 0.9559. Using surrogate benchmarking to evaluate the performance of the model on denoising epigraphical images, an SSIM score of 0.85 is obtained for the output of the U-Net model, demonstrating its ability to denoise epigraphical estampages. The proposed research lays down a framework for effectively denoising epigraphical estampages sourced from India. © 2024 IEEE.

关键词： Deep learning

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