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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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Human Genetic based Disease Identification 7

Human Genetic based Disease Identification

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bendigeri, Deepa Sakri, Leena Mural, Sakshi Hukkeri, Sakshi Tayannavar, Prithviraj Sdm College of Engineering And Technology Department of Information Science And Engineering Karnataka Dharwad India Sdm College of Engineering And Technology Department of Artificial Intelligence And Machine Learning Karnataka Dharwad India

ISBN: (纸本)9798350359299

Human genetic diseases come in a vast range, stemming from changes in the genetic makeup. They can manifest in various ways, affecting a person's physical, mental, and behavioral traits. It is crucial to identify the genetics behind these illnesses in order to create accurate diagnostic methods, comprehend their origins, and create personalized remedies. While every disease has a genetic component, the extent of its influence may differ. One example of a disease caused by a specific genetic mutation is cystic fibrosis. Fortunately, advancements in technology and techniques have greatly contributed to detecting detrimental genetic changes. Diseases may be fully analyzed using a graph-theoretic framework that portrays a network of diseases and disease genes, allowing us to build more focused and effective therapies. Cardiovascular disease is a pathological condition that is impacted by a combination of genetic and environmental factors. Genetics and epigenetics have a major influence in the genesis and expression of many diseases. Epigenetic modifications exert an impact on the expression of genetic material, without inducing alterations to its underlying nucleotide sequence. The discipline of epigenetics is an intriguing and continuously evolving domain within biology, which investigates the heritable modifications in gene expression that transpire without modifying the fundamental DNA sequence. Epigenetic modifications can exert an effect on the pathogenesis of cancer and several other diseases. Mutations, often known as pathogenic variations, can cause genetic conditions by altering gene material. DNA, the basic genetic material, contains genes, which determine distinct traits and instruct physiological activities. © 2024 IEEE.

关键词： Diseases

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Deep learning Approaches for Strengthening Network Intrusion Detection Systems 7

Deep Learning Approaches for Strengthening Network Intrusion...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kaulgud, Sanjeev P Chempavathy, B. Tejeswar, Nama Sai Vigneshwar, Chekka Kesava Kumar, Akuthota Rakesh Eswar, Akula New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331527822

Strengthening network security is a must in today's digital landscape. Existing Intrusion Detection Systems commonly make use of deep learning techniques such as Deep Neural Networks to identify malicious & anomaly activities. However, there are serious problems of overfitting and huge demand for computational resources in this system that hinder the efficiency and scalability of this system. It introduces an advanced form of an Intrusion Detection System (IDS) which integrates a hybrid machine learning algorithm with Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) along with other machine learning techniques to improve threat detection. This system would realize the recognition of established patterns as well as new anomalies about attacks by means of enhanced feature engineering and real-time AI-driven threat intelligence. It also addresses scalability for more extensive and complex configurations of the network as well as response mechanisms that automatically neutralize threats quickly. Then there is user behavioral analytics that helps in identifying insider threats. It is aimed at providing an all-embracing security solution that is strong and adaptable, greatly improving overall protection of the network. Hence, the effective execution of this project will thus provide a new benchmark for the effectiveness of IDS in delivering reliable security across different sectors as well as paving the way for further improvements in cybersecurity around the globe. © 2024 IEEE.

关键词： Network intrusion

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Automated Unified System for LPG Refill Booking & Lekage Detection: A Pervasive Approach 15

Automated Unified System for LPG Refill Booking & Lekage Det...

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

作者： Suraksha, P. Aishwarya Kumari, Kavana L. Dept.of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Dept.of Artificial Intelligence and Machine learning Vidyavardhaka College of Engineering Karnataka Mysuru India

ISBN: (纸本)9798331300579

Civilization is becoming increasingly aware of natural resources, it results in the use of propane and butane, and hence the use of *** India most people use the LPG *** per current government regulations, intense demand but shortage in production of LPG cylinder, once a new cylinder is booked we need to wait for some days to get it *** no cost-efficient technique is available which identifies when cylinder is about to get *** this paper we designed, developed, a proven Automated Unified System using smart Pervasive sensors, where LPG retailer requests for a new cylinder using Short Message Service (SMS) once it identifies cylinder will be empty in few days and also provides detection of gas leakage for better service to the consumers and reduce the scope of irregularities. © Grenze Scientific Society, 2024.

关键词： Liquefied petroleum gas

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Enhanced Image Processing for Automobile Accident Detection Using Deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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

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

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： Image enhancement

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UNet++ and Efficient Net: Hyperparameter Optimization Training Strategy for Precise Pneumothorax Image Segmentation 4th

UNet++ and Efficient Net: Hyperparameter Optimization Train...

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4th International Conference on Advanced engineering Optimization Through Intelligent Techniques, AEOTIT 2023

作者： Telang, Aparna S. Kashyap, Vijay M. Department of Electrical Engineering P.R. Pote Patil College of Engineering and Management Amravati India Department of Artificial Intelligence and Machine Learning DSATM Karnataka Bangalore India

ISBN: (纸本)9789819746538

The lungs play a crucial role in the respiratory analysis of human beings. Pneumothorax is a condition that arises from the leakage of air into the pleural space, resulting in the collapse of the lungs. This condition can impede respiratory movement, cause breathing difficulties, disrupt blood flow, lower blood pressure, lead to pleurisy and cardiovascular disorders, and in severe cases, pose a life-threatening risk. Therefore, the detection and timely treatment of pneumothorax are imperative. While pneumothorax is typically identified through the primary diagnostic imaging technique of chest X-ray (CXR) imaging, the effectiveness of treatment relies on a prompt review of radiographs. A computerized diagnosis system has the capability to identify pneumothorax in chest radiographic images, offering significant advantages in disease diagnosis. In the current study, a novel architecture comprising UNet++ and the pre-trained EfficientNet V4 model is proposed for the detection of pneumothorax regions in chest X-ray images. The novelty of the proposed method resides in its effective utilization of the hyperparameter optimization strategy. This approach involves training models with a diverse range of architectures and training parameters, enhancing the efficiency of the overall methodology. It helps in fine-tuning the model to achieve the best possible performance, ultimately contributing to more reliable and accurate segmentation results. Also, BCE dice loss is primarily considered as the main criterion to assess the model’s accuracy. The higher the BCE dice loss better the model and the output segmentation. This study gives the highest BCE dice loss of 0.89, and dice loss for validation images is 0.86. In order to achieve this value of BCE dice loss, we did a few iterations to zero in on the particular learning rate. Number of iterations have been carried out with different preprocessing of image, batch size, and learning rate to obtain the highest possible BCE dice los

关键词： Blood pressure

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Efficient Chromosome Abnormal Detection using Yolo-V8 model 3

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Umarani, V. Isaac, Augustian Premnath, K. Kannan, G. Sanjay, A. Hari Haran, L.K. Saveetha Engineering College Dept of Computer Science and Engineering TamilNadu Chennai India Saveetha Engineering College Dept of Artificial Intelligence & Machine Learning TamilNadu Chennai India

ISBN: (纸本)9798331523893

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders. © 2025 IEEE.

关键词： Deep learning

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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AN EXPLAINABLE AI MODEL IN HEART DISEASE CLASSIFICATION USING GREY WOLF OPTIMIZATION

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Scalable Computing 2024年第4期25卷 3139-3151页

作者： Varun, G. Jagadeeshwaran, J. Nithish, K. Sanjey, D S Ashick Venkatesh, V. Ashokkumar, P. Department of Cybersecurity and Internet of Things Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research India Department of Artificial Intelligence and Machine Learning Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research India

Heart disease is one of the world’s leading causes of death. It is estimated that around one-third of all deaths are caused by heart disease in the entire world. Recently many research works have focused on using machine learning models to detect and warn patients about the occurrence of heart disease at the early stage. However, machine learning models provide promising results, and the performance of the classification is affected by various reasons which include imbalanced training, and missing values. There are three main contributions of this research work. Firstly, missing values are addressed by employing a grouping of instances. Secondly, a dual filter based feature selection is introduced to pick the most effective features and lastly, we make of Grey Wolf Optimization for optimizing the hyperparameters of the machine learning models. Together, these contributions aim to improve the robustness and efficiency of machine learning applications by addressing missing data, optimizing feature selection, and fine-tuning model parameters. The accuracy of 98.41% indicates the superiority of the proposed classification which is more than 17.15% than the existing machine learning models. On the other hand, we use Explainable AI (XAI) methods to make our proposed model interpretable. © 2024 SCPE.

关键词： Feature Selection

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Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction 3

Exploratory Analysis of Heart Attack and Breast Cancer Early...

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3rd International Conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2024

作者： Vandana, C.P. Kashyap, M Shivani Yashas, D. Singh, Neha New Horizon College of Engineering Dept of Information Science & Engg. Bengaluru India New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331505714

This paper involves the development of a web based platform with an integrated machine learning algorithm to address problems pertaining to heart disease and breast cancer. It enables users to select either the heart attack or breast cancer datasets and use machine learning models such as Logistic Regression, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest and XG Boost. The website provides basic information about the datasets such as the total number of records in a dataset, number of classes in a dataset, and also allows users to generate graphs for better visualization of the data. The evaluation of the performance of each of the models is based on various metrics such as accuracy, F1 score, precision, recall and mean squared error. This way it is possible to assess the performance of the models in relation to each of the available data sets which makes it easy for users to pick the most effective model. There are also inputs for the users which would be their health indicators like age, sex, level of cholesterol, heart rate among others. Using these values as well as the picked model, the website estimates the possibility of the user developing heart disease or breast cancer. Apart from predicting health risks, there is an emphasis on the evaluation of health risks by different machine learning models and thus assists users in finding the most accurate prediction model regarding their healthcare problems. Overall, the website provides both predictive healthcare insights and a deeper understanding of how various algorithms perform in real-world scenarios. © 2024 IEEE.

关键词： K-Nearest Neighbor (KNN) Logistic Regression Predictive Analytics Random Forest Support Vector machine (SVM) XGBoost

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