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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

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Intelligent Automation & Soft Computing 2023年第7期37卷 455-470页

作者： Salah Alzghoul Othman Smadi Ali Al Bataineh Mamon Hatmal Ahmad Alamm Biomedical Engineering Department The Hashemite UniversityZarqaJordan Department of Electrical and Computer Engineering Norwich UniversityNorthfieldVermont05663USA Department of Biochemistry and Molecular Biology The Hashemite UniversityZarqaJordan National Centre for Big Data Science and Artificial Intelligence AmmanJordan

Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of *** using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following *** study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient *** analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for *** divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last *** then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for *** results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival *** optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after *** study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.

关键词： Aortic stenosis aortic valve replacement machine learning survival period enhancement artificial intelligence in cardiology

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Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries 18

Machine Learning Challenges of E-government Models of Cloud ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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Integrating Deep Learning with Case-based Reasoning for Enhanced Brain Tumor Detection: A Review 2

Integrating Deep Learning with Case-based Reasoning for Enha...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Krishna, Redrouthu Vamsi Dhatterwal, Jagjit Singh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350368413

Detecting brain tumors (BT) early is vital for effective treatment planning and improved patient outcomes. The fusion of Deep Learning (DL) and case-based reasoning (CBR) presents a promising solution for enhancing BT detection accuracy and reliability. DL methods, such as Convolutional Neural Networks (CNNs), excel in automatically extracting discriminative features from medical images, bolstering diagnostic capabilities. Case-based reasoning, on the other hand, leverages past diagnostic cases to provide context-aware decision support for new cases. This review study provides an overview of recent advancements in the integration of DL with CBR for brain tumor detection. Then discuss the strengths and limitations of both approaches and highlight how their integration can address key challenges in brain tumor detection, such as limited labeled data, interpretability of DL models, and the need for personalized diagnosis. Furthermore, review existing studies that have applied DLand CBR techniques individually and in combination for brain tumor detection. Finally, discuss challenges and future research directions and opportunities for leveraging this integrated approach to improve brain tumor diagnosis and patient care. In this, review total 55 papers are taken based on the Integrating DL with Case-Based Reasoning for Enhanced Brain Tumor Detection. In this, the research articles published between 2021 and early 2024 were taken into consideration for this study. © 2024 IEEE.

关键词： Convolutional neural networks

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Fine-tuning the Gemma model for Kaggle Assistant 1

Fine-tuning the Gemma model for Kaggle Assistant

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Zhalgasbayev, Arman Khauazkhan, Ali Sarsenova, Zhibek Sofrware Engineering Student Astana IT University Astana Kazakhstan Big Data Analysis Student Astana IT University Astana Kazakhstan Computer Engineering department Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

The emergence of advanced open-source large language models (LLMs) opens new opportunities for researchers and accelerates the development of generative AI. This study focuses on harnessing the potential of Gemma, Google's recently released open source LLM, to develop the Kaggle assistant through fine-tuning. The main objective of the study was to investigate the Low-Rank Adaptation (LoRA) fine-tuning method of Gemma model for building a robust AI assistant in Kaggle. Using the Gemma 2B variant, this study applies the Latent Consistency Model - LoRA method to improve interpretability and performance. The fine-tuning of the model achieved a sparse categorical cross-entropy (Loss) of 0.4943 and a sparse categorical precision of 0.7753. Although experiments with epochs and batch sizes revealed potential overbuilding after 10 epochs, the model shows acceptable performance in this range. The integration of LoRA fine-tuning enhances the model's ability to provide accurate recommendations and conclusions to users solving Kaggle problems, thereby improving the user experience and reinforcing its utility as a valuable resource for data scientists. Further refinement and adaptation of the Gemma model, especially through techniques such as LoRA fine-tuning, are critical to ensure its continued effectiveness in assisting Kaggle users in their data science endeavors. ©2024 IEEE.

关键词： Gemma model Kaggle LLM LoRA fine-tuning

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Lightweight Fog Attention Graph Convolutional Network for Secure data Aggregation Mechanism to Fog based Cloud Assisted IoT Network 8

Lightweight Fog Attention Graph Convolutional Network for Se...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Akilesh Kumar, S. Hariharan, P. Sukhel Ahamed, N. Gokilavani, S. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Ever Increasing demand of Cloud Computing paradigm has resulted in widespread development and deployment of multiple fog nodes to cloud assisted internet of things networks as it is highly capable of providing the users with on demand and low latency services through mobile collaborative devices in the edge of multiple clouds. Mobile Collaborative Devices is used for fog computing with integrated storage, computing and communication capabilities. However it offers improved efficiency and increased flexibility. Despite of multiple advantageous of fog based cloud assisted internet of things, security of the user data and their privacy can be compromised during data aggregation. In addition, it faces huge challenges in effectively aggregating the data and transmitting to the cloud server on establishing the strong security. In order to enhance the security of the user data and to preserve the privacy of the user information along establishing a secure data aggregation and transmission, a new light weight convolutional attention network is proposed in this article. It establishes secure data aggregation and data transmission to the distributed cloud data centers efficiently. Fog attention graph convolutional network leverages neighbour fog data efficiently and securely from dataset and represents in form of graph. Attention coefficient is assigned to each fog data represented in graph structure and attention score is calculated to each update of the nodes in graph. Specifically softmax function employs ID3 decision tree classifier and K anonymization mechanism to aggregates fog data on basis of updates of the attributes of the fog nodes in terms of the attention score and transmits aggregated data to the distributed cloud server. Further proposed model enhance the security of the data aggregation and transmission with index function through hash mechanism. Experimental analysis is carried out on the Fog assisted IoT medical (FIoMT) dataset. dataset composed of patient h

关键词： Fog computing

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Predicting Pre-Owned Bike Values with Machine Learning Techniques in Bangladesh 2

Predicting Pre-Owned Bike Values with Machine Learning Techn...

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2nd IEEE International Conference on data Science and Network Security, ICDSNS 2024

作者： Ferdous, Kazi Hassanul Arman, Md.Shohel Ahmed, Kazi Rifat Tasnim, Nusrat Mahmud, Imran Revathi, P. Daffodil International University Diu Data Science Lab Department of Software Engineering Dhaka Bangladesh Holy Cross College Department of Computer Science Tamilnadu Tiruchirapalli India

ISBN: (纸本)9798350373110

As the importance of sustainable practices in the automobile sector grows, it's critical to anticipate motorcycle prices and offerings. With so many variables to consider when buying a secondhand motorcycle-condition, mileage, brand reputation, model characteristics, etc.-accurate pricing prediction is essential for both customers looking for good values and sellers hoping to maximize profits. This study investigates the effectiveness of four machine learning techniques Decision Tree models, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Stochastic Gradient Descent (SGD) Regressions-in predicting motorcycle prices using a dataset obtained from motorcycle merchants. It's noteworthy that these methods are used with a hybrid architecture to improve prediction accuracy. Different approaches are compared and contrasted to find the best fit for the given dataset, and the difficulties and roadblocks that come with them are also discussed. After extensive testing, the decision tree model's effectiveness is shown with an amazing 97% accuracy rate, providing accurate price estimates that are essential for encouraging sustainable habits in the ever-changing Internet of Things environment. With an impressive accuracy rate, this methodology provides precise pricing estimations, which are critical for supporting sustainable practices in the automotive industry. © 2024 IEEE.

关键词： Motorcycles

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Attention based 1-D CNN system for detection of Freezing of gait (FOG) in Parkinsons Patients 15

Attention based 1-D CNN system for detection of Freezing of ...

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

作者： Pradeep Kumar, D. Sowmya, B.J. Vincent, Ashvin Pandiyan, Aravindh Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India Bangalore India

ISBN: (纸本)9798350370249

One common clinical symptom seen in Parkinson's disease (PD) patients is freezing of gait (FOG). It manifests as an irregular gait, marked by abrupt, involuntary stopping of movement during gait episodes. FOG entails a major negative effect on PD patients' quality of life. In this work, we analyze the movement data that is collected using the lower-back 3D accelerometer worn by the patients during FOG episodes. By employing machine learning methods to classify this data, we aim to accurately detect FOG trends. In particular, we focus on Deep Learning-based techniques, specifically an attention-based 1D Convolutional Neural Network (1D-CNN), due to their capacity to extract intricate features from unprocessed sensor input. Our results indicate that the proposed model achieves AUC scores of 0.9032 with respect to the TDCSFOG dataset and 0.8682 for the DeFOG dataset, for a combined average AUC of 0.8857. Mean Average Precision (mAP) scores for TDCSFOG and DeFOG reach 0.8388 and 0.779, respectively, for an average of 0.8089, showing the ability of the model to predict FOG occurrences over time. These findings suggest techniques based on Deep Learning enhance FOG detection, allowing clinicians to more effectively tailor treatments and care strategies for Parkinson's patients, thus enhancing their ability to move and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Accelerated Malaria Diagnosis with Deep Convolutional Neural Network 1

Accelerated Malaria Diagnosis with Deep Convolutional Neural...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Krishna Kumar, V. Geetha Devasana, M.S. Saravana Balaji, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India De Montfort University Kazakhstan Department of Business Data Analytics Almaty Kazakhstan

ISBN: (纸本)9798350359756

The disease known as malaria is a communicable illness that is spread by the bites of mosquitoes. There are already diagnostic approaches that include manually counting the amount of red blood cells (RBC) that are contaminated. This is accomplished by doing a microscopic examination of the stained blood cells of the individual who is suffering from the disease. However, this is a hard job that requires careful attention both visually and intellectually. In order to accomplish this, a comprehensive manual examination must be undertaken by an expert who is informed about the subject matter. It is possible that the implementation of deep learning algorithms will simplify the process of disease diagnosis and make this analysis more straightforward. Furthermore, because they are diagnosing malaria, they are expected to manage a vast quantity of data, which includes photographs of microscopic blood smears. This is because they are responsible for finding the disease. In order to get a better level of accuracy, it is necessary to train models that are more in-depth. This requires fast computing since it requires a substantial amount of computer resources. An accelerated convolutional neural network technique that is based on a graphics processing unit (GPU) is utilized by the Accelerated Malaria Diagnosis System (AMDS) that has been created in order to diagnose malaria. © 2023 IEEE.

关键词： Diseases

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Secure Channel Creation for Task Delivery in Vehicular Cloud Computing to Avoid Denial of Service (DoS) 3

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Meenambika, A. Pavalarajan, S. Lavanya, N. Anu Jerom, B. Ajith Divya, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331532420

Vehicular Cloud Computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks. © 2024 IEEE.

关键词： Cloud platforms

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