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Comparative Analysis of Transfer Learning Models for Brain Tumor Detection

Comparative Analysis of Transfer Learning Models for Brain T...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Jyoti Kumar, Anuj Sachar, Silky Computer Science and Applications Panjab University Chandigarh India Amity School of Engineering and Technology Amity University Mohali India

Brain tumors rank among the most dangerous and life-threatening conditions, requiring accurate and timely detection for efficient treatment. Conventional diagnosis techniques, which rely on radiologists' manual interpretation of medical pictures, might be time-consuming and susceptible to inconsistencies. To address these issues, deep learning (DL) models have become effective instruments for automating tumor classification, improving both efficiency and accuracy. In this manuscript, we evaluate the efficiency of various DL models in classifying MRI images as either tumorous or non-tumorous using the BR35H dataset, which includes 2,100 training images and 600 validation images. We assess nine pre-trained transfer learning (TL) models: DenseNet121, MobileNetV2, Inception-V3, MobileNetV3Large, Xception, DenseNet201, VGG16, AlexNet, InceptionResNetV2, and VGG16. Each model is fine-tuned and evaluated using precision, accuracy, recall, and F1-score metrics. Among all the techniques, Xception achieved the greatest accuracy at 98.33%, while MobileNetV3Large exhibited the lowest accuracy at 75.16%. This outcome showed the efficiency of advanced DL models in binary tumor classification, highlighting the importance of model selection in developing accurate and reliable diagnostic tools. Overall, the Xception model stands out as the most promising for automating the detection of tumorous and non-tumorous conditions in MRI images, offering a significant improvement over the traditional diagnostic method. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Diagnostic radiography

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Utilizing Deep Reinforcement Learning for Advanced Pattern Extraction in Big Data Analytics and Ontology Systems 2

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Karthik, A. Asha, V. Bhardwaj, Garima Nijhawan, Ginni Khan, Irfan Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Amity University Amity Business School Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement Learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors. © 2024 IEEE.

关键词： Deep reinforcement learning

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Machine Learning-Based Cybersecurity Optimization in Internet of Things-Enabled Autonomous Vehicles 1

Machine Learning-Based Cybersecurity Optimization in Interne...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Musa, Usman Ibrahim Musa, Aminu Ibrahim Galadima, Yakubu Ibrahim Kantunsung, Ramadan Osman Gupta, Sakshi Dua, Sakshi Lovely Professional University School of Computer Application Punjab India Esgt Benin University Department of Information Technology Cotonou Benin Al-Qalam University Department of Computer Science Katsina Nigeria University College of Engineering Osmania University Telangana India Amity University Amity Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350348637

The swift proliferation of Internet of Things-Enabled Autonomous Vehicles, or IoT-AVs, caused serious apprehensions about potential security weaknesses. In response, we provide a unique solution that improves cybersecurity in IoT-AVs by applying machine learning (ML) approaches. Three critical areas are the focus of our research: intrusion detection optimization (IDOpt), network reliability and latency optimization (NetOpt), and user authentication optimization (AuthOpt). Through biometric approaches which include fingerprint recognition, we accomplish powerful user authentication with 99% accuracy by using convolutional neural networks (CNNs). For Network Reliability and Latency Optimization, the Random Forest Regressor algorithm minimizes downtime and latency issues, improving vehicle performance with 97% accuracy. Additionally, we use the K-Nearest Neighbors (KNN) technique in intrusion detection optimization to protect IoT-AVs against harmful assaults. We use this ML model and obtain a 99% accuracy rate, allowing us to quickly identify and stop intrusions. The entire security posture of IoT-AVs is strengthened greatly by this optimization. To validate the effectiveness of our proposed approach, we compare our results with existing models and algorithms. The outcomes demonstrate the superiority of our ML-based optimizations over the previous approaches. Our integrated methodology establishes new benchmarks for security enhancement in IoT-AVs. © 2024 IEEE.

关键词： Adversarial machine learning

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An In-Depth Analysis of Network Security: Intrusion Detection and Prevention Systems

An In-Depth Analysis of Network Security: Intrusion Detectio...

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Delhi Section Conference (DELCON), IEEE

作者： Amritpal Kaur Rajesh Kumar Tyagi Shweta Sinha Prashant Vats Department of Computer Science and Engineering Amity School of Engineering and Technology Haryana India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The concept of making everything easily and widely accessible has revolutionized the network industry. Even with the rapid advancements in networks and information technology, we still have difficulty guarding against attacks and theft of our resources. Small organizations might not be affected by this, but industry, businesses, and national security are all seriously affected. Every day, organizations encounter more and more dangers in the form of malware, intrusions, etc. Despite the fact that corporations have implemented a variety of defences against these types of assaults, including intrusion detection and prevention systems (IDPS), many security breaches remain undiscovered. To understand the security risks and IDPS, we will first survey common security breaches and then discuss the different opportunities and challenges in this field. In this paper, we survey the overall progress of intrusion detection systems, including existing types, techniques, and architectures found in the literature.

关键词： Surveys Industries Prevention and mitigation Intrusion detection Organizations Network security Malware National security Information technology

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning

Learning Salient Features for Speech Emotion Recognition usi...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Dilsheen Kaur Anuradha Misra O.P. Vyas Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Department of Information Technology Indian Institute of Information Technology Allahabad

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)" to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework.

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IoT based Weather, Soil, Earthquake, and Air Pollution Monitoring System 7

IoT based Weather, Soil, Earthquake, and Air Pollution Monit...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Pal, Yogesh Nagendram, S. Al Ansari, Mohammed Saleh Kamlesh Singh Anto Gracious, L.A. Pratap Patil School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Artificial Intelligence KKR & KSR Institute of Technology and Sciences Vinjanampadu Andhra Pradesh Guntur India College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Bahrain Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India Department of Computer Science and Engineering RMK College of Engineering and Technology Tamilnadu Puduvoyal India Department of IT and Engineering Amity University Tashkent Uzbekistan

ISBN: (纸本)9781665464086

This research study shows how IoT technology can screen local meteorological conditions and share that information globally. Weather shifts cause extreme rainfall. A flood monitoring system uses NODEMCU ES P8266 to store and retrieve data and inform authorities of rising water levels using ultrasonic sensors and LEDs. S oil moisture affects crop growth. Its microprocessor and sensor improve soil moisture monitoring. An earthquake warning system can detect the slightest vibration before a major earthquake. Due to industry and autos, air quality is getting worse. Air quality and chemical content must be assessed using IoT since it has changed so much. Connected devices and enhanced sensor technology have transformed traditional environmental monitoring into a cutting-edge S mart Environment Monitoring S ystem (S EMS). This paper evaluates S EM aids and research investigations, including air quality, weather, soil, and seismic monitoring systems. S EM applications segment the examination, with a deeper dig into each section's sensors. Discussion findings and analyzed research patterns form the basis for the in-depth analysis that follows the comprehensive review and suggests key S EMS research implications. The authors studied how IoT, machine learning, and other sensor-based advancements have made environmental monitoring smart. © 2023 IEEE.

关键词： Internet of things

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AI-Powered Personalization and Emotional Intelligence Integration for Enhanced Service Marketing in Transformative Tourism Experiences

AI-Powered Personalization and Emotional Intelligence Integr...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Semwal, Rajeev Tripathi, Nandita Rana, Ajay Dafouti, Balam Singh Bairwa, Mahesh Kumar Mathur, Vipula Amity Institute Of Travel And Tourism Amity University Uttar Pradesh Uttar Pradesh India Amity Institute Of Psychology And Allied Sciences Amity University Uttar Pradesh Uttar Pradesh India Amity School Of Engineering And Technology Amity University Uttar Pradesh Uttar Pradesh India School Of Computer Sciences & Information Technology Computer Science Uttarakhand Open University Haldwani India State Institute Of Hotel Management Hospitality And Tourism Department Rajasthan Jhalawar India Apex University English Humanities And Arts Deprtment Rajasthan Jaipur India

ISBN: (纸本)9798350366846

Tourism service providers may offer more personalized, sensitive, and emotionally engaging experiences with Artificial Intelligence (AI) and Emotional Intelligence (EI). This paper examines its effects, limitations, and tourist business research opportunities. The research framework shows how AI-driven personalization, emotional intelligence integration, predictive motivation analysis, and dynamic learning and adaptation affect service delivery. Real-world AI and EQ applications in customer service, marketing, sales, operations, and destination management increase consumer engagement, happiness, loyalty, and competitive advantage. Privacy, measurement, assessment, human-machine interaction, technical accessibility, and ethical concerns must be addressed. Further study may be needed to establish ethical frameworks, make technology more accessible, discover a balance between humans and robots, perfect measuring tools, and balance personalization with privacy. Despite these challenges, AI-driven personalization and emotional intelligence could transform the customer experience and spur long-term growth and innovation in the tourism industry. © 2024 IEEE.

关键词： Competition

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SVM-ERFE: An Enhanced Method for Identification of Metastatic and Relapsing Nature of Breast Cancer 4

SVM-ERFE: An Enhanced Method for Identification of Metastati...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Abhijit Kumar, Sumit Riou, Kevin Choudhury, Amitaya Singh, Gurinder Tripathi, Sudhanshu School of Computer Science University of Petroleum and Energy Studies Dehradun India Thapar Institute of Engineering and Technology Patiala Punjab India Polytech Nantes University of Nantes France Pundit Deendayal Energy University Gujrat Ahmadabad India Amity University Uttar Pradesh India Amity University Department of It and Engineering Tashkent Uzbekistan

ISBN: (纸本)9798350387490

Classifying breast cancer into metastatic non-metastatic is crucial for a women that undergoing for surgery, because of the heavy treatment associated with metastatic cancers. With the advancement of micro array technology, identifying gene signatures from large genes dataset becomes an efficient approach to predict or classify cancerous genes. We proposed an algorithm based on support vector machine using enhanced recursive feature extraction mechanism (SVM-ERFE). The proposed algorithm identifies most significant genes signatures and thus prognosis of metastatic/relapsing nature among breast cancer patients. In order to enhance the accuracy, we kept allow to varying the number of genes after first filtering. Several experiments has performed over metastatic-295 breast cancer patient's datasets and relaps-GSE2034 dataset, and achieved the best accuracy 100%±0.0 for 158 and 307 gene signatures after first filtering respectively. Further, the model has validated using 10-fold cross validation using evaluation metrics accuracy, TPR, TNR. Moreover, proposed model can increase the dortor's confidence into the results. © 2024 IEEE.

关键词： Lung cancer

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Crowd Counting Model Using Convolutional Neural Network

Crowd Counting Model Using Convolutional Neural Network

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Patwal, Akshita Diwakar, Manoj Tripathi, Vikas Singh, Prabhishek Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Uttar Pradesh Amity School of Engineering& Technology Noida India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Crowd Counting is being used for public safety, effective management of the crowd in elections or pilgrimages, music concerts. Recently there was a stampede in January 2022 in a temple in India where due to overcrowd many people were killed. Also, to stop the spread of pandemic crowd counting is proving to be beneficial in public places. Counting manually is a tedious task and it may produce false results, since it takes a long time. In crowded photos, objects appear to be partially surrounding each other as the density of people increase in the frame. Crowd counting is having limitations such as occlusion and background clutter. To solve this difficulty, earlier approaches relied on labelling complex density maps to understand the scale variation implicitly. Data preparation can be time expensive, and training these deep models might be problematic owing to a shortage of training data. As a result, we suggest an alternate and new method for crowd counting. The proposed model in this paper counts the number of people in the given image using a convolutional neural network based on ResNet50. © 2022 IEEE.

关键词： Convolutional neural networks

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A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection

A Review on Swarm intelligence & Evolutionary Algorithms bas...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Bhandari, Sachin Pathak, Sunil Jain, Sonal Amit Deshmukh, Varun Amity School of Engineering & Technology Amity University Dept. of Computer Science & Engineering Rajasthan Jaipur India Sardar Patel University Pg Dept. of Computer Science & Information Technology Vallabh Vidyanagar India Mpstme Nmims Shirpur Dept. of Computer Engineering Shirpur India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Diabetic retinopathy has overtaken cataracts as the primary cause of new blindness globally. Diabetics are more likely to develop cataracts, visual loss, glaucoma, excessive intraocular pressure, and, most importantly, diabetic retinopathy (DR). If blood vessels in the retina are compromised, vision loss is irreversible. The patient may not exhibit any symptoms early on, and by the time they do, the damage has already been done. Early diabetes treatment helps to retain vision and permits a patient to see. Diabetic retinopathy is a worldwide health problem. To address the medical community's requests for early identification of diabetes and other illnesses, several professionals have advocated a computer assisted diagnosis technique. In this work, image processing techniques and image classifiers that sort images based on the status of the disease will be used to describe automated ways to look at retinal images for important signs of diabetic retinopathy. There are compelling motivations to create retinopathy risk reduction models and strategies that can be used widely. The difficulty of acquiring accurate diabetic retinopathy at a reasonable cost needs a major investment in creating and testing computer-assisted diagnosis (CAD). This study looks at the different stages, traits, and types of models that may be used to reduce the risk of diabetic retinopathy and detect it early using Evolutionary computing and Swarm optimization. © 2022 IEEE.

关键词： computer aided diagnosis

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