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Artificial Intelligence Assisted Improved Design to Predict Brain Tumor on Earlier Stages using Deep Learning Principle

Artificial Intelligence Assisted Improved Design to Predict ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Priyadharshini, K. Krishnamoorthy, P. Bala Sundara Ganapathy, N. Karthikeyan, Kavitha Uma Maheswari, S. Peddaveni, Ramesh Jeppiaar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sasi Institute of Technology & Engineering Department of Computer Science and Engineering Andhra Pradesh Tadepalligudem India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Dundigal HyderabadTelangana India

ISBN: (纸本)9798350303452

Brain- Tumors are the second most common reason of cancer today. Numerous people are at risk from cancer. The medical community requires a rapid, automated, efficient, and trustworthy method for detecting tumors, such as brain tumors. Early diagnosis is crucial for effective treatment. If a tumor can be detected early enough, medical professionals can remove the patient from harm's way. This research makes use of novel image processing and deep learning method to predict the brain tumor in an intelligent manner with the power of AI, which is called as Supportive Intelligence for Tumor Detection (SITD). Numerous tumor patients have been saved thanks to this app's accurate diagnosis and therapy. Unchecked cell growth constitutes a tumor's defining feature. As brain tumor cells multiply, they starve healthy brain cells and tissues of the nutrition they need to survive. When trying to locate a tumor in a patient's brain, neurosurgeons must now review MR pictures manually. The goal of this research was to compile a comprehensive literature evaluation on the use of MRI to detect brain cancers for future investigation. This research looked at how advanced learning, transferable learning, and quantum computer learning may be used to analyze brain tumors. Topics included brain tumor anatomy, publically available datasets, augmentation approaches, segmentation, feature extraction, classification and more. The resulting section shows the outcome efficiency of the proposed approach SITD by means of following metrics such as: Training-and- Testing Accuracy, Training-and- Testing Loss. © 2023 IEEE.

关键词： Tumors

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Banking Security and Customer Experience: Cloud Computing-Enabled KYC in the Digital Era using ANN Model

Banking Security and Customer Experience: Cloud Computing-En...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Nithya, A. Muthukumaran, D. Varadarajan, Mageshkumar Naarayanasamy Sangeethalakshmi, K. Mahalakshmi, R. Srinivasan, C. St.Joseph's Institute of Technology Department of Information Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Capital One Glen AllenVA United States R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350378092

The research work investigates the innovative notion of IoT-enabled Know Your Customer (KYC) and the revolutionary possibilities of Internet of Things (IoT) in the banking sector. Artificial Neural Network (ANN) models may discover complicated data correlations, making customer verification more accurate and efficient. Banks may automate KYC processes using cloud-based ANN models to speed up client onboarding and transaction approvals in the digital age. The demand for better protection measures, quicker client onboarding, and constant compliance monitoring is rising as the banking industry moves online. Financial institutions may provide a more modern and streamlined client experience by adding IoT devices and technology into the KYC procedure. The system will look at how IoT-based biometric authentication techniques, including face recognition, voice analysis, and geolocation verification, may provide strong identity verification while protecting client data. In this article, the system explores how to give clients more agency via the IoT by integrating wearable devices for safe banking interactions and remote KYC. It also emphasizes the importance of IoT in real-time compliance monitoring, which helps financial institutions spot and stop fraudulent acts as soon as it occurs. This study foresees a future in which an IoT-driven KYC transformation improves banks security, customer experiences, and regulatory compliance efficiency. © 2024 IEEE.

关键词： Sales

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Machine Learning Approaches for Predicting Heart Failure from Echocardiography Data 10

Machine Learning Approaches for Predicting Heart Failure fro...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Yadav, Kanchan Aswal, Upendra Singh Michaelson, Jacob Laxminarayanamma, K. Arora, Deepika Singh, Pankaj Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

This study examines how to use ultrasound data to predict cardiac failure using a deductive methodology, interpretive theoretical framework, and a descriptive design of research. The foundation of the investigation is secondary information collecting from several healthcare organizations. Clinical factors including age, medical history, and test results are collected from echocardiography and combined with properties like the fraction of ejection, thickness of the walls, and valvular function. The main predictive tool utilizes the linear regression method, chosen for its clinical applicability and comprehension. Accuracy, sensitivity, particularity, and AUC-ROC measures are used to assess the algorithm's performance, giving a thorough evaluation of its discriminatory capacity. Results show a strong relationship between certain echocardiogram features and the risk of heart failure. Integrating clinical data improves predictive accuracy and provides a more complete picture of the condition of the patient. The main factors influencing heart failure prediction are clarified through the examination of coefficients with feature significance scores. The study's conclusions are clinically applicable and give healthcare professionals useful information for prompt action and individualized patient treatment. Throughout the investigation process, ethical issues including data protection and informed consent are carefully addressed. By offering a structured framework for using ultrasonography data in coronary artery disease prediction, this research advances the developing field of coronary medicine. Given the context of managing heart failure, it highlights the possibility for improved managerial choices and patient satisfaction. © 2023 IEEE.

关键词： blood pumping crippling cardiovascular deep learning heart failure Numerous methods

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Exploring the Experimental Possibilities of AI based Real- Time Virtual Reality Assisted Touchless Home Appliance Control 1

Exploring the Experimental Possibilities of AI based Real- T...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Kumar, C. Ashok Gnanasekar, A. Ramya, R. Narmatha, S.U. Arunadevi, R. Ravi, C.N. Engineering Mlr Institute of Technology Department of Electronics and Communication Hyderabad India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Physics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350384246

The design and execution of home automation systems have been greatly enhanced by recent technological breakthroughs, which have increased the capabilities and usefulness of embedded systems. There is a significant demand for smart home automation systems that include Virtual Reality (VR). One possible use of the proposed technology is to let the owner operate their home appliances through virtual reality. Using this is a breeze. In this work, we offer a modular, practical, and secure automation control structure for smart homes that is tied to virtual reality and has a cheap price tag. In this study, we take a simple look at how virtual reality may be used to regulate voltage for common home equipment including lights, fans, air conditioners, motor controls, and more. An AI-based Virtual Appliance Control System (AIVACS) is the name given to the proposed system because of its foundation in AI logic. In order to determine how effective the proposed model is, it is cross-validated using a standard Home Appliance Control System (HACS). The hardware has been assembled and tested in a scaled-down prototype with three options for controlling it: illumination, motors, and a fan in a domestic setting. The primary rationale for this research is the significant practical benefit that this VR-based hands-free operation would provide to physically impaired and elderly individuals, who wouldn't be required to physically go to the location of the switch. © 2024 IEEE.

关键词： Domestic appliances

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Enhancing Radiology Diagnosis through Convolutional Neural Networks for computer Vision in Healthcare 1

Enhancing Radiology Diagnosis through Convolutional Neural N...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Badhoutiya, Arti Pokhariya, Hemant Singh Michaelson, Jacob Laxminarayanamma, K. Yadav, Vijay Kumar Krishna, Om Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The transformative power of Convolutional Neural Networks (CNNs) in radiology diagnostics is examined in this study, with a focus on interpretability, effectiveness, and ethical issues. With an altered DenseNet architecture, the CNN performs admirably in terms of particularity, sensitivity, as well as accuracy. Its superiority over conventional methods is validated by comparative analyses, which highlight efficiency gains. Nonetheless, interpretability issues highlight the necessity of sophisticated methods in addition to continuous model improvement. Integration issues like interoperability and radiologists' training lead to suggestions for teamwork. Systematic consideration of the ethical implications is carried out, necessitating extensive frameworks. Refinement of architectures, interpretability, alongside ethical considerations need to be prioritized in future work for responsible CNN deployment in radiology diagnostics. © 2023 IEEE.

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Building Indoor Temperature Prediction Using the IoT and Machine Learning 2

Smart Building Indoor Temperature Prediction Using the IoT a...

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2nd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2023

作者： Vincent, Robin Rohit Sarkar, Swagata Irence, Shalom Prakash, R.B.R. Senthil Rama, R. Suganthi, D. School of Computer Science & Engineering and Information Science Computer Science & Engineering Presidency University Bengaluru560064 India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai 44 India EEE Karunya Kits / CBE India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India DMI College of Engineering EEE Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Science Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350397635

Due to fast expansion and improved lifestyle, the need for useable energy has skyrocketed in recent decades, notably in the construction industry. Several factors, such as local climate, building makeup, and energy consumption habits, affect a structure's efficiency in using energy. In this paper, we aim for forecasting the interior temperature of smart buildings by combining IoT with popular machine Learning algorithms. This prediction model has been developed using online learning techniques to increase its adaptability to novel inputs. This paper details a methodology for creating an analytical model that may be utilized for smart building interior temperature forecasting by merging the IoT with some popular machine learning methods. This prediction model was built with an online-learning strategy, so it may be used for a dataset with which the developers are unfamiliar. To ensure the precision of the method, this article uses Machine Learning to test it using real-world sensor data. In order to verify the methodology, the study conducts experiments using Machine Learning on collected, real-world sensor data. The study then proposes incorporating the following method into an Edge Computing based IoT architecture in order to make the building operational in an energy-efficient manner. The learning framework was validated with data collected from an Internet of Things network installed in a home over the course of a summertime. The learnt model's efficacy has been evaluated, and the model's predictability of interior temperature changes has been quantified. The paper demonstrates the viability of the proposed Learning framework for thermal models based on the Internet of Things and provides a pragmatic answer to the problem of supplying accurate thermal models for use in the temperature controls of smart buildings of the future. © 2023 IEEE.

关键词： Energy utilization

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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