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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Munir, Fatima Nafees, Fouzia Mahmood, Jabar Chaudhry, Shehzad Ashraf Javaid, Maryam Batool, Khadija University of Sialkot Dept. of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates International Institute of Science Arts and Technology Dept. of Computer Sciences Gujranwala Pakistan

ISBN: (纸本)9798331532895

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods. © 2024 IEEE.

关键词： Agile DSDM Kanban Quality Enhancement Scrum SPM XP

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Thermal Performance Prediction of Metal-Foam Heat Sink Using Genetic Algorithm Driven Neural Network 2

Thermal Performance Prediction of Metal-Foam Heat Sink Using...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Chandan, K. Singh, Rimjhim Padam Nagaraja, K.V. Kumar, Priyanka Amrita School of Engineering Bengaluru Dept. of Mathematics India Amrita School of Computing Bengaluru Department of Computer Science and Engineering India University of Texas at San Antonio Department of Computer Science United States

ISBN: (纸本)9798350314168

Electronic device temperature management is crucial for maximum performance as well as its longer life and metal foam heat sinks (MFHS) are widely employed in electronic cooling due to their large specific heat transfer surface area and fluid mixing capabilities. However, due to the complicated and random geometrical character of MFHSs, precisely predicting their performance is difficult and costly. Several deep learning approaches have been used to address this difficulty, combining input features such as metal foam shape, heat sink dimensions, and coolant flow conditions to forecast thermal-hydraulic performance. This paper offers a unique approach for parameter estimation that combines Genetic Algorithm with an Artificial Neural Network (ANN) model. The Genetic Algorithm is used to optimize the ANN model's parameters thereby, increasing its accuracy in forecasting the friction rate and Nusselt number of MFHSs. The Genetic Algorithm seeks for the best set of parameters that minimizes prediction errors by iteratively evolving a population of candidate solutions using methods influenced by natural selection and genetic variation. The outperforming findings show that Genetic Algorithm can be used to estimate parameters in complex systems, specifically in predicting the thermal-hydraulic performance of MFHSs. © 2023 IEEE.

关键词： Neural networks

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Classification of Diabetes using the Biologically Plausible Network of Spiking Neurons 1

Classification of Diabetes using the Biologically Plausible ...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Hussain, Irshed Thounaojam, Dalton Meitei Dept. of Computer Science & Information Technology Bhubaneswar India National Institute of Technology Silchar Dept. of Computer Science & Engineering Silchar India

ISBN: (纸本)9781665454933

Diabetes is a disease that is becoming more common everywhere every day. The increased blood sugar level caused it to start. This sort of illness may be chronic and manifest when the patient's body is unable to produce enough insulin or fails to use it. The numerous computer or electronic-based systems were identified using various categorisation strategies for predicting and diagnosing diabetes. The 'third generation' of the neural network, i.e., the Spiking Neural Network (SNN), is getting much attention in the research community due to the akin dynamics of biological neurons, energy efficiency, and computational power. SNN is the future for bridging the gap between neuroscience and the Machine Learning (ML) community. The power of SNN can be beneficial in classifying diseases in medical science. This paper's experimentation is done on automatic diabetes prediction. We have observed that the SNN produces significantly superior results in terms of accuracy, computational cost and biological plausibility than that of the state-of-the-arts through careful experimentation on the Pima Indian Diabetes dataset drawn from the Kaggle. The proposed model of SNN uses the SpiFoG learning algorithm, an efficient, biologically plausible supervised learning algorithm, to train SNN models using a metaheuristic approach that uses genetic algorithm (elitist floating-point type) with a hybrid crossover along with the dynamics of the neuron model, and random delay in the synapse model. © 2022 IEEE.

关键词： Neurons

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Implementing a Novel Blockchain Algorithm for Enhanced Data Integrity in Cloud Systems 2

Implementing a Novel Blockchain Algorithm for Enhanced Data ...

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

作者： Balaji, V. Ambika, S. Varshadevi, S. Rajasekaran, R. Thalapathi Sangeetha, J. Chennai India E.G.S.Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Saveetha School of Engineering SIMATS Dept. of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering and Technology Thiruvalluvar Tirupachur India

ISBN: (纸本)9798350384277

Keeping data safe and sound in cloud systems is important. So, new ways need to be found so private information can't get changed or seen without permission by wrong people. This research gives a new blockchain method to make data reliability better in cloud systems. We show how our suggested method is better and more useful than others in keeping data safe. We do this by doing a deep study and comparing with other ways that are currently used. When we look at how many transactions a system can handle, the time it takes for things to happen and its ability to grow bigger without problems, our suggested method does better than other solutions in Comparing Performance. It handles 1000 transactions each second, with a time delay of only 10 milliseconds. It can work well for up to one hundred nodes on the network while still showing it's efficient and able to keep growing bigger over time. The Data Comparison shows that our suggested way is very good at finding out false information, and it can find 98% of this. Besides, it quickly recovers data with a downtime of only 2 hours. This makes it different from old ways by making sure data can't be changed and there are strong checks on who did what. Security Check shows our new plan works well. It stops people spending money twice, stands up against cheating tactics and keeps a strong Byzantine Fault Tolerance (BFT) at 99.9%. The Proof of Stake agreement is both easy on energy and safe. Security of smart contracts is also talked about, to make them safer. © 2024 IEEE.

关键词： Cloud security

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN) 7

Fingerprint generation and authentication though Adaptive co...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammad Nabeel Zehra, Syeda Sundus Baber, Alina Siddiqui, Maria Andleeb Ned University of Engineering and Technology Dept. of Computer Science & Information Technology Karachi Pakistan Sir Syed University of Engineering and Technology Dept of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data. © 2023 IEEE.

关键词： Generative adversarial networks

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： Qubits

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Virtual Reality Based Medical Training Simulator and Robotic Operation System

Virtual Reality Based Medical Training Simulator and Robotic...

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2022 International Conference on Recent Progresses in science, engineering and Technology, ICRPSET 2022

作者： Rahman, Md. Mustafizur Ishmam, Md Fatin Hossain, Md. Tanvir Haque, Md. Emdadul Dept. of Information and Communication Engineering Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350332704

In the medical sector learning about the human body and practicing surgeries, the medical students need a real human body which is very costly. Repeatedly practice or undone after practice is almost impossible with the human body. Human anatomy, physiology, etc. subjects are very hard to understand and learn from books and images. Learning surgery on living humans is sometimes unethical and trainee surgeons has to struggle to learn rare surgeries. To solve these problems we use virtual reality (VR) to build a medical training simulator that can provide medical students better understanding and learning about the human body through 3D models of the body. Students can get an interactive learning experience and see how organs work from the molecular level and perform several operations. Since this is a simulation everything a new learner will do can be undone and re-practiced. On the other hand skilled surgeons are not available in all places in the country especially in remote places. A patient who needs the critical surgical treatment has to travel to a city to take the treatment by the expert surgeons. To solve this problem in this project we also connect a robot to our VR based system. A skilled surgeon can operate in the virtual environment from a distant place using the robot. The vision system of the robot can be realized by the surgeon through VR headset and he or she feels like, he or she is present there. Surgeons can control the robotic hands and tools necessary to perform the surgery from the remote place. This will reduce the trouble of patients traveling from remote places to city area for surgery. We implement the demo system in the lab and show some results which indicate the applicability of the proposed system. © 2022 IEEE.

关键词： Robotic surgery

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bale, Ajay Sudhir Arulananth, T.S. Saranya, S. Gadag, Tejashwini Sathiamoorthy, J. Ponnuru, Sudhakiran New Horizon College of Engineering Dept. of Ece Karnataka Bengaluru India Mlr Institute of Technology Department of Electronics and communication Engineering Telangana Hyderabad500043 India Bms Institute of Technology & Management Dept. of Mathematics Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Rmk Engineering college Department of Computer science and Design Kavarapettai India

ISBN: (纸本)9798331543617

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time. © 2024 IEEE.

关键词： Electronic health record

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