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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

Computer Systems science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability 8

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Reddy, M.Jaganmohan Ramu, V. Grace Shalini, T. Vincysahaba, R. Devaki, M. Athiraja, A. Telangana Secunderabad India Telangana Hanamkonda India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Computational Intelligence Chengalpattu India St.Joseph College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Velammal College of Engineering and Technology Department of Eee Tamil Nadu Madurai India

ISBN: (纸本)9798350367904

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions. © 2024 IEEE.

关键词： Precision agriculture

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The Necessary Requirements for Utilizing AI in data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Predictive Modeling and Analysis of Campus Placements Using Machine Learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector Machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach 2

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kandan, M. Sarkar, Swagata Sharma, Meenakshi Rajeswari, V. Poonkuzhali, P. Srimathi, S. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur India Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India Global Group of Institute Department of CSE Amritsar India Panimalar Engineering College Information Technology India R.M.D Engineering College Kavaraipettai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350317558

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Earthquake LSTM Machine Learning

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Parasitized Malarial Infected Cell Detection through Variance Scaling Kernel Initializer Conv2D 4

Parasitized Malarial Infected Cell Detection through Varianc...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Devi, M. Shyamala Umanandhini, D. Joshi, Aparna Dontamsetti, Gowtham Shreyas, S. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Pimpri Chinchwad College of Engineering Computer Science and Engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350343670

Medical pathologists physically examine Plasmodium falciparum parasites infected blood cells that have been infected through a magnifying glass as part of the traditional diagnostic process, which is time-consuming and liable to mistakes. CNN and advanced technology have recently been used to conduct research on optimizing this procedure to identify orientation and geometrical information from blood sample particle images. With this view, this research recommends Variance Scaling Kernel Initializer Conv2D (VSKIC) Sequential model for predicting the infected malaria cell. The Malaria Cell Images dataset from KAGGLE, which contains 27,558 visuals of plasmodium parasites has been used for execution in the suggested VSKIC model. For the purpose of predicting a malarial cell infection, images are preprocessed and fitted using the VSKIC model. Three convolutional layers merged with max pooling layer, and three dense layers that are weight initialized with variance scaling kernel initializer are used as preprocessing steps on the malaria cell images for the purpose of identifying the presence of affected malarial tissue. The picture files were separated into training and evaluation images, and the training Malaria Cell images were used to evaluate the effectiveness of the VSKIC model along with the standard conV2D paradigms. While comparing the suggested model with the available CNN methods, the execution observations made with Python on a V100 Nvidia GPU workstation using 64 and 250 training intervals demonstrates that the proposed VSKIC model predicts with the higher accuracy of 98.96% and Area under curve of 97.8%. © 2024 IEEE.

关键词： accuracy AUC ConV2D initializer kernel

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Adaptive Attention-Enhanced Transformer With Modified Graph Cuts (AAET-MGC) Algorithm for Cardiovascular Disease Diagnosis 2

Adaptive Attention-Enhanced Transformer With Modified Graph ...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Vaanathi, S. Palanisamy, C. Steephan Amalraj, J. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam Tamilnadu Erode India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam Tamilnadu Erode India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam Tamilnadu Erode India

ISBN: (纸本)9798350368413

One of the core prerequisites in the diagnosis and treatment of cardiovascular diseases is accurate segmentation of the myocardium walls and small vessels in cardiac MRI datasets. Traditional segmentation methodologies often do not tackle this complexity of cardiac structures and their variability across MRI images. This may further enhance overfitting and local minima problems during training of deep learning models for cardiac image segmentation. Against this backdrop, a new hybrid approach is introduced called Adaptive Attention Enhanced Transformer with Modified Graph Cuts. This methodology is devised to merge the local feature extraction using a Convolutional Neural Network with the global context awareness provided by the transformer encoders further enhanced by attention mechanisms;user interest is oriented toward the most relevant features. The AAET-MGC model integrates an adaptive energy function within a modified graph cuts framework with spatial and intensity information, aiming at refining the segmentation results. This hybrid approach uses the power of deep learning in an initial segmentation process and classical graph cuts for accurate and smooth boundary detection. The method is evaluated against a comprehensive cardiac MRI dataset with an accuracy of 97%, avoiding overfitting and local minima. The AAET-MGC approach outperforms competing methods in the accurate segmentation of thin and complex structures, conductive to better diagnostic accuracy for the related clinical applications. © 2024 IEEE.

关键词： Image segmentation

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical data

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Computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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A BPNN-Based Method for Regional Competitiveness: Taking the Guangdong-Hong Kong-Macau Greater Bay Area as an Example 2nd

A BPNN-Based Method for Regional Competitiveness: Taking the...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Wang, Mini Han Li, Pengsheng Wang, Fengling Zhou, Xiaoshu Wu, Qing Chen, Guoqiang Lin, Zhiyuan Zeng, Peijin Xiao, Qide Beijing Institute of Technology Zhuhai China Faculty of Data Science City University of Macau Macau China Department of Big Data and Artificial Intelligence Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Hezhou University Hezhou China Gree Polytechnic Zhuhai China Zhuhai Science and Technology Development Promotion Association Zhuhai China

ISBN: (纸本)9789819721436

Guangdong-Hong Kong-Macau Greater Bay Area (GBA) reveals a significant opportunity for self-developing and regional competition since China’s government regards it as one of the most significant developing strategies. However, cities in this area are enhancing comprehensive competitiveness at different levels. This paper proposed a competitiveness evaluation model based on the entropy approach and built a competitiveness measuring and predictive model based on the BPNN machine learning algorithm. The research results illustrate that comprehensive competitiveness is approximately increasing over time, which shows a trend of eastward development. Hong Kong, Guangzhou, Shenzhen, Macau, and Dongguan are the top-5 cities during the three years. On the contrary, Zhongshan is the least competitive city, however, Zhongshan‘s comprehensive competitiveness emerges at the highest increasing rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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