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Dual Channel Convolutional Neural Network for Dementia Disease Prediction 2

Dual Channel Convolutional Neural Network for Dementia Disea...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Hussein, Layth Naveen, I.G. Aditya Sai Srinivas, T. Mukesh, S. Durgadevi, G. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Biomedical Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Dementia disease is a complex neurological condition that is affecting millions of population around the world. Dementia is known as a umbrella term because it does not represent a specific disease, it describes a range of cognitive impairments. Which are interfered significantly with the individual’s ability to function independently in their daily life. As a person’s age increases, the risk of dementia development also increases. This disease primarily affects the memory, thinking, and communication skills. The traditional methods failed to diagnose the disease accurately in early stage. This paper proposes a Dual Channel Convolutional Neural Network (DCCNN) to predict and classify the dementia disease. This algorithm is employed with the two different convolution layers that are internally consists of three convolutional layers and three fully connected layers and these layers are trained with the dataset first, and that are capable of processing two different type of data or features that are in that particular dataset simultaneously by utilizing two separate pathways or channels that are within the same model or architecture. Further, this method combines the features and provides the accurate prediction of the dementia. The proposed DCCNN model obtained 93% for R-fMRI dataset compared to the existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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AI -Driven Detection of Gastrointestinal Disease based on Advanced Deep learning Techniques

AI -Driven Detection of Gastrointestinal Disease based on Ad...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Sakshi V. Izankar Chetan Puri Rushikesh Burle Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Esophageal, gastric, and colorectal cancers (CRC) are some of the gastrointestinal (GI) cancers threatening global health with CRC being particularly concerning due to its high fatality rate. While early diagnosis is crucial in improving treatment efficiency, barriers such as image noise and visual subjective assessments during diagnostics such as colonoscopies often render it inconceivable. For better diagnostic accuracy in detecting gastrointestinal disease via endoscopic images, the research explores whether highly advanced artificial intelligence (AI) models such as Vision Transformers (ViTs) and pre-trained DenseNet201 can be effective. We compare these models to popular deep learning techniques such as Support Vector machines (SVM), ResNet50, VGG19, and GoogleNet. At a training accuracy of 99.9% and a validation accuracy of 98.8%, experiments on the KVASIR dataset indicate that Vision Transformer is superior to other models since it can learn long-range dependencies. The robustness of deep learning models for identifying GI disease is exemplified by the superior performance of DenseNet201, ResNet50, and InceptionV3, all of which possess high accuracy, precision, and recall values. The paper illustrates the potential of Vision Transformers, when combined with transfer learning and pre-trained CNN models, to revolutionize endoscopic diagnoses and provide a valuable resource to help physicians manage gastrointestinal conditions more effectively.

关键词： Deep learning Training Support vector machines Computer vision Visualization Accuracy Transformers Gastrointestinal tract artificial intelligence Residual neural networks

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Privacy-Preserving Data Mining Methods for Sensitive Information 3

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving Data Mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big Data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable Patterns Data Privacy Data Repositories Homomorphic Encryption and Utility Privacy-Preserving Data Mining Sensitive Information

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A Hybrid Deep learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Passenger Convenience: An Efficient Speech Driven Enquiry System for Railway Stations

Enhancing Passenger Convenience: An Efficient Speech Driven ...

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Women in Innovation, technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Leena Sakri S R Biradar Mitali Pramod Kulkarni Swarna Patilkukarni Sahana K Department of Artificial Intelligence and Machine Learning SDM College of Engineering and Technology Dharwad India Department of Information Science and Engineering SDM College of Engineering and Technology Dharwad India

ISBN: (数字)9798350383287

ISBN: (纸本)9798350383294

This paper presents a speech-based query system for railway stations. The system allows passengers to ask their queries in the form of speech and receive answers in the form of speech. It provides answers to a range of frequently asked queries, such as train information, seat availability, and PNR status, and is designed to be comprehensive and intuitive. Through the use of a combination of natural language processing, speech recognition, and database querying, the system can generate prompt and accurate responses. The speech recognition component translates the passenger's speech input into text. After parsing the text input, the natural language processing component determines the precise question the traveller is posing. The database querying component retrieves the relevant information from the railway database and generates a speech output tailored to the passenger's query. Most queries receive a response in less than 10 seconds, making the system accurate and efficient. This implementation of a speech- based query system at train stations holds promise for enhancing the overall passenger experience. By allowing passengers to ask their queries as a speech, the system can make it easier for passengers to get the information they need quickly and easily.

关键词： Accuracy Databases Entrepreneurship Speech recognition Speech enhancement Rail transportation Natural language processing

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Classification for Jaundice Symptoms Using Improved Dragonfly with XGBoost Model

Classification for Jaundice Symptoms Using Improved Dragonfl...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Kumar Pareek, Piyush Vidyalakshmi, K. Prasad, Anoop N. Diggi, Padmavathi Deepak, H.A. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Sri Siddhartha Institute of Technology Department of Information Science and Engineering Tumakuru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Navkis College of Engineering Department of Electronics and Communication Engineering Hassan India

ISBN: (纸本)9798350301595

The yellowing of the skin is a sign of neonatal hyperbilirubinemia, often known as jaundice. The most common method for screening for Hyperbilirubinemia in newborns is a visual examination;however, collecting blood specimens is the gold standard for identifying the condition and its harshness. Albumin-bound bilirubin was dissolved, producing the yellow hue. Too much haemoglobin breakdown results in bilirubin that the liver can't process. There are two ways for detecting bilirubin, an invasive one and a non-invasive one. Both methods have their advantages and disadvantages, but the invasive one needed more blood samples and was less pleasant to the patient. Neonatal jaundice is a physiological condition that affects about 80% of newborns. Because of this, hyperbilirubinemia occurs. Hence, it is important to check bilirubin levels on a frequent basis to determine whether patients require treatment and to identify those who may be at risk. The present continuous bilirubin measuring equipment is not only less precise than its predecessors but also more costly. In this research, we offer a diagnostic system for illness prediction using an enhanced version of the Extreme Gradient Boosting (XGBoost) classifier. A classifier's effectiveness relies heavily on the accuracy with which its hyperparameters are tuned. We employed the very effective hyper-parameter optimisation approach known as enhanced dragonfly optimisation (IDFO) on the XGBoost hyper-parameters. To liken the presentation of the suggested model to that of other replicas, tests are carried out. © 2023 IEEE.

关键词： Diagnosis

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Human Genetic based Disease Identification

Human Genetic based Disease Identification

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International Conference on Inventive Computation Technologies (ICICT)

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

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

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.

关键词： Pathology Genetic mutations Medical treatment Epigenetics Physiology Environmental factors Gene expression

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A Review on the Applications of machine learning Algorithms for Mental Stress Detection

A Review on the Applications of Machine Learning Algorithms ...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Renuka Motghare Shivam Bhoyar Prateek Verma Abhay Tale Aditya Barhate Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes

关键词： Accuracy machine learning algorithms Reviews Soft sensors Human factors Mental health Transforms Speech recognition Real-time systems Strain

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Predicting Stock Market Trends Using Deep learning: A Case Study of Pakistan Stock Exchange

Predicting Stock Market Trends Using Deep Learning: A Case S...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Maaz Balouch Faizan Hamayat Humza Fazal Abbasi Muhammad Qasim Javed Ali Mehrban Rana Fayyaz Ahmad Department of Business Studies Namal University Mianwali Pakistan Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center National Center for Physics Islamabad Pakistan Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center National Center for Physics Pakistan School of Electrical and Electronics Engineering Newcastle University Newcastle United Kingdom

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

This research investigates the short-term stock price prediction in the Pakistan Stock Exchange (PSX) using Deep learning (DL) techniques. By integrating historical stock data, economic indicators, and market news, the research aims to address the limitations of traditional stock analysis methods. Data was collected and pre-processed from various sources, including the PSX, economic indicators, and market news. Four techniques were implemented: ARIMA, LSTM, BI-LSTM, and a hybrid CNN-BI-LSTM. Their performance was evaluated using RMSE, MAE, and MAPE metrics. Comparative analysis revealed that BILSTM outperformed the other techniques, with RMSE, MAE, and MAPE scores of 0.9298, 0.6099, and 13.9443, respectively. Despite ARIMA's lower accuracy, it had the fastest inference time, followed by LSTM, CNN-BI-LSTM, and BI-LSTM. Results indicate that using comprehensive data with DL models significantly improves stock price forecast accuracy, highlighting the potential of technological innovation in enhancing financial market decision-making. These DL algorithms can provide valuable insights for investors and analysts in the PSX, leading to better decision-making and more accurate economic predictions.

关键词： Deep learning Measurement Accuracy Economic indicators Biological system modeling Decision making Predictive models Prediction algorithms Data models Stock markets

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AI-Enhanced Quad-Wheeled Robot for Targeted Plant Disease Surveillance in Greenhouses

AI-Enhanced Quad-Wheeled Robot for Targeted Plant Disease Su...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jadav, Nilay Chhajed, Harsh Patel, Upesh Jani, Devesh Barai, Aum C. S. Patel Institute of Technology Charotar University of Science and Technology Department of Electronics & Communication Engineering Gujarat India C. S. Patel Institute of Technology Charotar University of Science and Technology Department of Artificial Intelligence and Machine Learning Gujarat India

ISBN: (纸本)9798350308167

technology is rapidly changing modern agriculture, resembling the Green Revolution of the early 20th century, which ushered in advanced agricultural technologies and practices, resulting in substantial increases in crop yields. The practical 4X4 greenhouse bot, attesting to the cohesive integration of the Internet of Things (IoT), machine learning algorithm and Robotics, is at the pinnacle of this change. These smart bots, equipped with various environmental sensors, continuously monitor, and provide real-time alerts, ensuring consistent plant health. Simultaneously, the introduction of powerful computer vision techniques, particularly models like YOLOv8, has heralded a new era of accuracy in agricultural practices. This study offers a quad-wheeled AI-powered robot with a Jetson Xavier NX powered camera module for targeted disease surveillance in greenhouses. It constantly monitors environmental conditions via IoT-equipped sensors, for early disease detection in poly-house farming hold the potential to boost farming efficiency and yields. © 2023 IEEE.

关键词： Agricultural Automation Robotics Semi-Autonomous Waypoint Navigation YOLOv8

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