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检索条件"机构=Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning"

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AI-Powered MRI Scan Analysis for Enhanced Patient Hospitality: Real-Time Report Generation for Faster Diagnosis - A Systematic Review

AI-Powered MRI Scan Analysis for Enhanced Patient Hospitalit...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Jaganath, M. Shri, G. Ramya Bharani, B.I. Kumar, S. G. Manoj Haris, S. Saravana Karneya, B. M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798331521394

MRI scans play a vital role in the diagnosis and treatment of numerous medical conditions, and efficient analysis of these scans using AI is necessary for enhancing patient outcomes. This systematic review explores the application of deep learning models to AI-based MRI scan analysis, with an emphasis on real-time report generation for improving patient hospitality and speeding up the diagnosis process. We compare a range of deep learning networks such as ShuffleNet, VGG, EfficientNet, and AlexNet for examining the accurate model for MRI scan data classification. The results indicate that AlexNet performs better than the rest of the models, registering a remarkable 98% accuracy, beating VGG (96%), EfficientNet, and ShuffleNet (both 97%). AlexNet presents better performance in classifying critical classes like Elbow Mucosa, Elbow Crack, and Knee Benign Tissue with ideal precision, recall, and F1-scores. Yet, it indicates somewhat poor performance for Knee Muscularis and Knee Serosa. The results indicate that AlexNet provides a very effective tool for real-time automatic MRI scan analysis, leading to quicker diagnosis and better patient care. This AI-driven method, when incorporated into clinical processes, can really enhance diagnostic productivity and patient care. © 2025 IEEE.

关键词： Electrotherapeutics

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An Analysis and Detection of Depression from Textual Data using Deep learning Methods 2

An Analysis and Detection of Depression from Textual Data us...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Chaithra, I.V. Sunitha, M.R. Department of Computer Science Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present. Mental ill health is highly risky, stirring more than 300 million population. Because of this reason more focus of research is on this area. A statement by American Psychiatric Association "6.715 adults (6.7%) is get affected in any given year. One-sixth of people(16.6%) will experience depression in their life." Studies said that Women are in greater proportion to likely experience depression when compared to men. One among three women will share a significant depressive symptoms in their lifetime. Written text reflects its impact on language usage. The count of afflicted individuals can be lessened, using an advanced machine learning techniques and the availability of test data related to depression. This paper proposes different preprocessing techniques. Feature extraction techniques such as BoW(Bag of Words), Word2Vec, Glove, N-Gram, LIWC and LDA. Classification techniques such as Naive Bayes, SVM, LR, Adaboost and RF. © 2024 IEEE.

关键词： Deep learning

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DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHITECTURE 10

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHI...

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10th International Conference on AI and the Digital Economy, CADE 2024

作者： Paulraj, Getzi Jeba Leelipushpam Princess, P. Joyce Beryl Jebadurai, Immanuel Johnraja Celshia, S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837241880

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively. © The Institution of engineering & Technology 2024.

关键词： Blood vessels

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Solutions for Cervical Cancer: Deep learning Models for Accurate Stage Prediction 2

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Yadav, Roshan Kumar Kumar, Divyam Kadam, Kalyani Devi, Seeta Gangarde, Rupali Department of Artificial Intelligence and Machine Learning Pune India Department of Obstetrics and Gynecological Nursing Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350383652

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques-Local Binary Pattern (LBP), ResNet, and VGGNet-an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset. © 2024 IEEE.

关键词： Contrastive learning

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Survey on Classification of Skin Diseases Using machine learning Techniques 3

Survey on Classification of Skin Diseases Using Machine Lear...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Nirupama Virupakshappa Sharnbasva University KLB Department of Artificial Intelligence and Machine Learning Kalaburagi India Sharnbasva University KLB Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350309409

Skin is our body's most important organ;skin disorders are currently a common and serious problem due to patients' sensitive skin, as well as physical and psychological consequences. Skin disease symptoms might differ greatly from person to person and may be permanent or temporary, severe or painless. Skin disease symptoms include clogging, inflammation, rashes, irritation, and other changes in skin appearance. Early detection of skin disorders is ideal because it reduces the patient's actual expenses while additionally preventing illnesses from developing and spreading. In recent years, artificial intelligence (AI) has been used to diagnose skin disorders using machine learning algorithms and a large amount of different skin data stored in database. Recent studies on skin disease identification and classification using predictive algorithms, mechanisms, and specific systems resulted in higher accuracy. machine learning will play an important and promising role in real-time diagnosis of dermatological diseases in the future. © 2024 IEEE.

关键词： machine learning

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DROPEX: Disaster Rescue Operations and Probing using EXpert drones 8

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

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

作者： Kannan, Kausthub Awati, Aditya N. Rao, Smruthi S. Malagi, Vindhya P. Dayananda Sagar College of Engineering Department of Computer Science And Design Bangalore India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331505462

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations. © 2024 IEEE.

关键词： Drones

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Node Coverage and Connectivity Optimization in Heterogeneous Wireless Sensor Networks Using Improved Flower Pollination Algorithm 4

Node Coverage and Connectivity Optimization in Heterogeneous...

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

作者： Mahankali, Saritha Kesavan, R. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Vardhaman College of Engineering Department of Computer Science & Engineering Telangana India Rajalakshmi Engineering Department of Artificial Intelligence & Machine Learning Chennai India

ISBN: (纸本)9798331529635

Wireless sensor networks (WSNs) rely on energy-conserving routing protocols due to the limited power and communication capabilities. The LEACH protocol has seen extensive usage despite its homogeneous network foundation, leads inadequate signal coverage. To solve the issue, this research proposes an improved flower pollination algorithm (IFPA) improves flower pollination algorithm (FPA) addresses issues with slow convergence and local optima. This research presents an enhanced clustering technique that may be utilized in heterogeneous sensor networks. Its main feature is the ability to adjust the status of data transmission. Once the observed information intensity above the predefined threshold, the data transmission connection is initiated and data is sent to the sink by the cluster heads, which filter the perceived information. Otherwise, cluster nodes keep track of the data and wait for the next batch of nodes to provide data. The good news is that the simulation results show a network lifespan that is several times longer than the LEACH protocol. The IFPA method stands out because it increases coverage rates while drastically decreasing the cost of deploying networks. As a result, the suggested method might find many uses, helping to lower costs and improve the performance of wireless sensor networks. © 2024 IEEE.

关键词： Sensor nodes

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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