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Enhancing Alpha thalassemia Screening: A Comparative Study of Multiple Machine learning Classifiers and Interpretation using Explainable AI 4

Enhancing Alpha Thalassemia Screening: A Comparative Study o...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Bharath, M. Gowtham, S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350349900

this study introduces an innovative paradigm for Alpha thalassemia detection by integrating machine learning, hyperparameter tuning, and explainable artificial intelligence (XAI) techniques. through a comparative analysis, boosting algorithms, especially XG Boost, demonstrate remarkable efficacy in accurately identifying Alpha thalassemia conditions. the iterative hyperparameter tuning process enhances the final XG Boost model's accuracy to an impressive 90.11%. the incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. this research's significance lies in its potential to transform Alpha thalassemia screening, offering precision, resilience, and transparency. the amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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intelligent Monitoring System for Subway Environmental Safety based on Edge Computing 24

Intelligent Monitoring System for Subway Environmental Safet...

引用

4th International conference on Internet of things and Machine learning

作者： Zhu, Jiashuang Yan, Zixin Luo, Chenran Zhou, Xiangdong Liu, Xia Li, Yazhuo Jianghan Univ Coll Intelligent Mfg Wuhan Peoples R China

ISBN: (纸本)9798400710353

In order to ensure the safe and efficient operation of subway stations, it is necessary to obtain air quality data and passenger flow information in subway stations in real time. In this paper, an intelligent monitoring system for subway environmental safety has been built, which consists of three modules: subway air quality detection module, subway passenger flow detection module and data display storage analysis module. Particulate matter (PM), CO2 and formaldehyde which are hazardous to the health of passengers and staves has been detected by subway air quality detection module. the subway passenger flow detection module can obtain on-site video, utilize object detection algorithms to acquire real-time passenger flow information in subway station. All data is displayed, stored and analyzed through the local wireless LAN upload server. through the data interaction between each edge device and the server, air quality data and passenger flow information in the subway environment can be monitored in real time, so as to realize the digitalization, precision and intelligence of subway operation management.

关键词： Object detection Air quality testing Edge devices Subway Station

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Swarm Intelligence Based Feature Selection and Machine learning Methods for Future Railway Traffic Prediction 4

Swarm Intelligence Based Feature Selection and Machine Learn...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Radhika, C. Hanirex, D.Kerana Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

An essential prerequisite for proactive and anticipatory real-time regulation of railroad traffic is the accurate forecast of train delays, or deviations from the schedule. To manage the viability of timetable realization, traffic controllers must forecast the trains' arrival times in (or approaching) their region. Rescheduled travel, traffic control, data on passengers, and effective route design all rely upon the ability to predict train locations in both space and time in real time. the study introduces a data mining system designed to forecast train traffic. the system utilizes a dataset that includes the scheduled times of all trains and the relationships between them, along with weather characteristics. In order to improve the system's accuracy in predicting future traffic, research was proposed that would use Swarm Intelligence-based feature selection techniques as Harris Hawk optimization (HHO). To ascertain the best predictive models in terms of predictive capability, machine learning techniques namely Bi-directional Long Short-Term Memory (Bi-LSTM) with entity embedding are utilized. It has been demonstrated that the suggested algorithm for processing and analyzing rail traffic is more efficient than alternative approaches and has shown an accuracy rate of 92.3% and precision of 93%. © 2024 IEEE.

关键词： Swarm intelligence

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Canine Demodicosis Detection in Dogs using Deep learning 4

Canine Demodicosis Detection in Dogs using Deep Learning

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Chinta, Anuradha thota, Durga Sravanthi Bypilla, Aswini Durga V.R.Siddhartha Engineering College Department of Cse Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350349900

Detecting canine diseases holds immense significance for timely and precise diagnosis, facilitating effective treatment and care. In this proposed system, canine disease detection is achieved using deep learning techniques such as CNN, focusing on normal images of dogs. the utilization of these images provides valuable insights into the dog's condition and enables the identification of various diseases, including mange. Deep learning techniques are employed to automatically extract features from these normal images and classify them into different disease categories. Transfer learning is applied to leverage pre-trained models and enhance the performance of the disease detection system. the proposed methodology shows promising results with an accuracy of 97% in accurately detecting and classifying canine diseases, providing a valuable tool in diagnosing and treating dogs more effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on intelligent anti-collision system of inland waterway lock based on deep learning 4

Research on intelligent anti-collision system of inland wate...

引用

4th International Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Xia, Chunrong Xia, Kai Jiangsu Union Technical Institute Wuxi Institute of Communications Technology Jiangsu Wuxi214151 China Wuxi Lead Intelligent Equipment Co. Ltd. Jiangsu Wuxi214000 China

ISBN: (纸本)9798331528676

Once a ship carrying dangerous goods collides with the lock, the consequences are very serious. Based on the channel project of the Beijing-Hangzhou Canal and the Yangtze River, this paper systematically describes the active lock collision avoidance system based on deep learning platform and its key technologies, introduces the technical route of the active collision avoidance system. We develop the system software architecture, control core algorithm and the active lock collision avoidance device based on the blocking device. It provides technical support for the industrial application of the active anti-collision system of inland lock. ©2024 IEEE.

关键词： Deep learning

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intelligent Short Video Recommendation System Based on Deep learning 4

Intelligent Short Video Recommendation System Based on Deep ...

引用

4th IEEE International conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Zheng, Xiangwang Zeng, Xiaxia Fuzhou Melbourne Polytechnic Fuzhou350121 China School of Computer and Big Data Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350360721

We designed an intelligent short video recommendation system using deep learning technologies. the system integrates content-based and collaborative filtering recommendation algorithms to enhance recommendation accuracy and personalization. A hybrid recommendation model was constructed including rating and comment data extraction modules., features fusion., and rating prediction. the system has a three-tier architecture for interaction between users and the recommendation engine. Key functions include user management., video playback., rating., commenting., and recommendation. the recommendation engine is powered by deep learning to suggest personalized videos based on user behavior and content features. © 2024 IEEE.

关键词： Recommender systems

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Transfer learning and CNN-based Framework for Intrusion Detection in Highway Internet Toll Systems 24

Transfer Learning and CNN-based Framework for Intrusion Dete...

引用

4th International conference on Internet of things and Machine learning

作者： Li, Zhuo Zhong, Guoqing Hu, Yong Xu, Jianbin Jiang, Nan Zhang, Yuejin East China Jiaotong Univ Sch Informat & Software Engn Nanchang Peoples R China Jiangxi Transportat Res Inst Co Ltd Intelligent Transportat Dept Nanchang Peoples R China Traff Monitoring & Command Ctr Dept Transportat Jiangxi Prov Nanchang Peoples R China

ISBN: (纸本)9798400710353

Nowadays, the frequency and intensity of cyber attacks have escalated significantly. Traditional network security defenses, which predominantly rely on static, predefined rules to distinguish between legitimate and malicious network traffic, have proven inadequate in identifying complex and sophisticated network intrusions. the integration of artificial intelligence (AI) technologies offers a pathway to enhancing the reliability and effectiveness of these defenses. Convolutional neural networks (CNNs), a subset of deep learning models, have achieved notable advancements in image processing, thereby gaining considerable scholarly attention. By harnessing the capabilities of CNN models, complex network attacks can be efficiently detected through the transformation of network traffic datasets into image representations. this study proposes an intelligent Intrusion Detection System (IDS) model, designed to enhance the security of highway internet-based toll systems, which integrates optimized CNN architectures, transfer learning, and ensemble learning methodologies. the ensemble model is trained on the transformed data and validated using the CICIDS2019 network dataset. the experimental results indicate a detection accuracy of 99.91%, substantiating the feasibility and effectiveness of the proposed approach within this research.

关键词： IDS Convolutional Neural Networks Transfer learning Ensemble Model Data Transformation

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Investigation on Deep learning and Machine learning Approaches for Antenna Design Based on Radar Signal Processing 4

Investigation on Deep Learning and Machine Learning Approach...

引用

4th International conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Govindasamy, Santhakumar Bhushan, Kavi Nijhawan, Ginni Kumar, B Santhosh Chandrashekar, Rakesh Issa, Ali Ashoor Sri Krishna College of Technology and Tamilnadu Electronics and Communication Engineering India Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

this paper gives a complete review of the use of Deep learning (DL) and Machine learning (ML) approaches in the area of antenna design for radar data processing. It analyzes the potential of DL and ML to overcome the constraints of conventional antenna design methodologies, especially in the face of complicated environmental variables and the necessity for interference reduction. By employing sophisticated computational techniques, the research reveals how these AI-based approaches may considerably boost the design and performance optimization of radar antennas. the integration of neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning into the design process offers promise for constructing adaptable and efficient radar systems. Empirical findings from the research illustrate the durability of ML models, notably Support Vector Machines (SVMs), in forecasting antenna performance, stressing its resilience even in high-noise environments. this investigation's results are crucial for the progress of intelligent radar systems and lay the way for future improvements in the industry. © 2024 IEEE.

关键词： Support vector machines

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Maximizing Privacy in Reinforcement learning with Federated Approaches 4

Maximizing Privacy in Reinforcement Learning with Federated ...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Mehta, Shiva Sarpal, Sumeet Singh Chitkara University Centre for Research Impact &outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350349900

Data protection issues and security in the age of technological advancement have become problematic, mainly because these methods are increasingly widespread. this paper covers critical problems of Federated Reinforcement learning (FRL) and shows how this technique could provide a viable solution to the issue. We are attempting the use case scenario of the Federation Q-learning in the Labyrinth difficulties here. It ensures that technological requirements are adapted at all levels and keeps the information secret. A trial has been made to test the application of IRL on AI training, enabling the scientists to acquire actual proof of the advantages of FRL against the theoretical approaches in training, which are the majority. the main quantitative facet of this case uses pretty scientific statistics. Research results from conventional Q-learning and our variation, codel bean Q-learning, are significant in drawing conclusions and gaining insights. the federated q-learning presented scores around 25% higher than the q-learning with an agent, which is one more advantage than the q-learning using an agent. Just as Federated Q-learning obtains fast policy optimization by reducing convergence time by 2 5%, parallelism also brings about quicker and more effective exploration of the Q-table. Federated Q-learning is no exception to the general rule that this model has an advantage in ensuring data privacy, yielding an evident four-point improvement in the data privacy score. this shows that this technology quickly becomes a trusted ally and can anonymize data without compromising users' privacy and security. Moreover, in the final stage of our work, the implementation of the model pretty soon caused an improvement in computer efficiency by 40%. the results showed that the current traditional AIL techniques suddenly faced five times more risks concerning privacy and security. © 2024 IEEE.

关键词： Data Privacy FedAvg Approach Federated learning Privacy Preservation Reinforcement learning Security

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A study of time-attention-directed convolutional neural networks in autism 4

A study of time-attention-directed convolutional neural netw...

引用

4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Wang, Changbao Shi, Yuhu Shanghai Martime Univ Coll Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798400716645

Autism is a complex neurodevelopmental disorder with varying degrees of severity and symptoms among individuals. Previous studies on dynamic functional connectivity did not account for the differential contributions of various time windows to diagnosis. To address this issue, this paper proposes a multi-instance weakly supervised learning approach applied to 174 fMRI datasets from 74 individuals with autism and 100 healthy controls. A novel time window attention mechanism is introduced to weigh the significance of different time periods in the diagnostic process. Specifically, features are extracted using a ResNet model to capture high-frequency information from time windows, and a time window attention mechanism assigns varying weights to different time windows, which are then fused together. the proposed method achieves an accuracy of 88.89% and a recall of 75.00% on a publicly available dataset, surpassing baseline performance by 16.67% in accuracy and 12.50% in recall. Comparable results with other methods demonstrate the significant potential of time window attention in autism classification. this paper introduces, for the first time, a weakly supervised learning approach for autism detection using time window attention, potentially providing a new framework for detecting other mental health disorders.

关键词： time window attention multi-instance weakly supervised learning approach Autism

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