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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Malik, Hassal Sonia Sial, Umaira Ahmad Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer. ©2024 IEEE.

关键词： Lung cancer

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Polycystic Ovary Syndrome Prediction through CNN based Image Analysis: A Deep Learning Based Approach 8

Polycystic Ovary Syndrome Prediction through CNN based Image...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthik, Yazhini Sruthi, R. Sujithra, M. Coimbatore Institute of Technology Department of Computing - Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women during their reproductive years. It is characterized by irregular menstrual cycles, excessive hair growth, and the presence of cysts in the ovaries. The diagnosis of PCOS typically involves a combination of clinical evaluations and laboratory tests. The paper proposes a novel approach using Convolutional Neural Networks (CNNs) for predicting PCOS through the analysis of ultrasound images. The dataset comprises images obtained from ovarian ultrasound scans of both PCOS-positive and PCOS-negative individuals. These images undergo preprocessing and augmentation techniques such as rescaling, shearing, zooming, flipping, and rotation to enhance the diversity of the training data. Multiple CNN-based models with varying architectures are constructed, altering parameters such as the number of convolutional and pooling layers, activation functions, and other hyperparameters. The models are trained and validated using a split of the dataset into training and validation sets, with metrics like loss and accuracy monitored during training to assess model performance. Upon completion of training, the models are tested using unseen data to evaluate their predictive capabilities. The results demonstrate that deep learning models trained on ultrasound images can accurately predict the presence or absence of PCOS. Such models hold promise in aiding healthcare professionals with early diagnosis and management of PCOS, ultimately improving patient care and outcomes. © 2024 IEEE.

关键词： Ultrasonic imaging

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Classification of Fetal Health on Cardiotocograph data using Machine Learning Techniques 15

Classification of Fetal Health on Cardiotocograph Data using...

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

作者： Deepa, M.S. Sujithra, M. Department of Computing-Data Science Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

Pregnancy and fetus development is an incredibly intricate biological process that can go wrong when the health of fetus or the mother gets ***, India has the highest number of stillbirths, with an estimated 5,92,100 deaths per year, and a WHO estimated rate of 22 per 1000 total *** is one procedure used to check whether the fetus is growing as *** diagnostic procedure measures the mother's uterine contractions and the fetus' heartbeat, usually in the third trimester of pregnancy when the fetus heart is fully *** paper aims to classify the results into one of three states (physiological, suspicious or abnormal) based on cardiotocographic data using machine learning algorithms and compare the accuracies of different ML classification models such as logistic regression, k-nearest neighbor, decision tree, random forest classifier and support vector machine. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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Peer Tutoring and its Impact on Academic Achievements

Peer Tutoring and its Impact on Academic Achievements

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Aqeel, Muhammad Hassan, Manahil Ghani, Affaf Zaheer, Manahil Anjum, Bilal Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504465

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring. © 2024 IEEE.

关键词： Federated learning

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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A Comprehensive Study on Current Trends in Unsupervised Machine learning Algorithms and Challenges in Real World Applications 14

A Comprehensive Study on Current Trends in Unsupervised Mach...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Velvadivu, P. Sujithra, M. Priyadharshini, R. Department of Computing - Data Science Coimbatore Institute of Technology India

In this technology fueled world, everyone is using smart devices, electronic gadgets, wireless products etc. and huge amounts of data are being generated, collected, and stored in the databases. To efficiently process and intelligently analyze the huge amount of data, the knowledge about subfield of Artificial Intelligence that is, Particularly Machine learning (ML) is required. There are various types of machine learning and its algorithms have been introduced to handle real world scenarios. This paper discusses the Comprehensive survey based on methodologies, techniques, algorithms, applications, and challenges faced by unsupervised machine learning and how unsupervised learning techniques can be helpful in real world business and environment. Thus, this study’s key contribution is explaining the principles of different unsupervised machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. © Grenze Scientific Society, 2023.

关键词： Unsupervised learning

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Adaptive sail fish optimizer based optimized region selection for robust video steganography

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Identification of Personality Traits by Machine Learning Analysis of Signatures and Handwriting

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SN Computer science 2024年第5期5卷 497页

作者： Sharma, Dinesh Parashar, Vivek Parashar, Amrita Department of Data Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computing Science and Engineering VIT Bhopal University Bhopal India

Personality plays a crucial role in personal growth across various aspects of life, professional pursuits, individual success, family dynamics, and love compatibility. Analyzing handwriting is a vital method for understanding one's personality traits. Yet, the significance of a signature surpasses regular handwriting, holding four times its weight. Personality encompasses thoughts, behaviors, emotions, and desires, profoundly shaping an individual's success. Graphology, a scientific discipline, explores these traits by examining both handwriting and signatures, offering valuable insights into human character. Graphologists utilize these analyses to gain deeper insights into an individual's character. Signatures serve as public representations of a person, offering valuable glimpses into their social and external lives, thereby aiding individuals in gaining a profound understanding of themselves. This paper aims to employ machine learning in conjunction with graphology techniques to predict personality traits and provide insights into one's life. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Personality traits Signature and handwriting analysis Signature identification

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention 2

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Maniraj, S.P. Thamizhamuthu, R. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331505745

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78%. These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation. © 2025 IEEE.

关键词： Feature Selection

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