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A new design of a digital filter for an efficient field programmable gate array using quantum dot technology

Materials Science and engineering: B 2024年 300卷

作者： Taghavirashidizadeh, Ali Ahmadpour, Seyed-Sajad Ahmed, Suhaib Jafari Navimipour, Nima Ramkrishna Kassa, Sankit Yalcin, Senay Tehran Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Chitkara University Institute of Engineering and Technology Punjab India Department of Electronics and Communication Engineering Model Institute of Engineering and Technology Jammu India Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pune India Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

Digital filtering algorithms are most frequently used to implement generic-based Field-programmable gate arrays (FPGAs) chips, which are used for higher sampling rates. In the filtering structure, delay and occupied areas play a vital role. Since the existing structures suffered from shortcomings such as high delay and high occupied area, implementing a high-performance digital filter circuit with high speed and low occupied area based on unique technology can significantly improve the performance of whole FPGA structures. One of the best technologies to implement this vital structure to solve these shortcomings is quantum-dot cellular automata (QCA) technology. This paper presents several new efficient full adders for digital filter applications based on quantum technology, including a multiplier, AND gate, and accumulator. The QCADesigner 2.0.3 tool is used to create and validate the suggested designs. According to the results, all designed circuits have simple structures with few quantum cells, low area, and low latency. © 2023

关键词： Nanocrystals

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Neural Network Approach For Classification And Detection Of Chest Infection

Neural Network Approach For Classification And Detection Of ...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Mustafa Muhanad M. Salih Muhammet Çakmak Department of Computer Science and information system AL Mansour University College Baghdad Iraq Department of Electrical Electronics Engineering Karabuk University Turkey

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

Advances in computer technology have had a profound impact on our lives and the way we see the world. The healthcare industry is advancing thanks to the use of cutting-edge computer technology, which has transformed how numerous ailments are diagnosed and treated. The number of people suffering from chest-related illnesses is increasing at an alarming pace as a result of a wide range of conditions, including air pollution. Medical applications of image processing have emerged due to data collection tool development. It is now possible to make out the diagnosis through study of the features from medical investigation reports for a group of patients. That reduces the time and cost of the diagnosis, which may help plenty of people who are unable to access regular medical facilities due to intolerable cost. In this paper, automatic chest infection diagnosis is being diagnosed using a Neural Network. Two models are used, namely the Artificial Neural Network and the CNN Neural Network. The models are tested using NIH x-ray chest image data. Results are reported with 96.7% and 99.20% accuracy from the Artificial Neural Network and CNN, respectively.

关键词： Measurement Industries Costs Image processing Medical treatment Mean square error methods Convolutional neural networks

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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The three dimensional dosimetry imaging for automated eye cancer classification using transfer learning model

The three dimensional dosimetry imaging for automated eye ca...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Murtuza Ali Mohammed Vazeer Ali Mohammed Rakesh Ramakrishnan Mehmood Ali Mohammed J. Logeshwaran Maheswaran S Department of Computer & Information System Lewis University Illinois USA Department of Information Technology University of The Cumberlands Union City CA USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Kongu Engineering College Erode Tamil Nadu India

computer vision methods utilizing transfer learning can offer a promising approach for automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest advances in AI have encouraged researchers to use deep learning models for automated cancer classification. This paper presents a novel approach using three-dimensional dosimetry imaging for automated ocular cancer classification using transfer learning. The approach uses a combination of dataset augmentation methods. The approach is adapted from the popular Inception V3 CNN architecture. Two different feature extraction approaches, namely mean and maximum pooling, are compared while extracting features from the output of the CNN layers. The extracted features are fed to the classifier which uses the SVM with a linear kernel as its decision system. The effectiveness of this method is evaluated on a set of 20 healthy ocular images and 26 suspicious ocular images. The overall classification accuracy achieved with this approach is 94.85%. The results obtained demonstrate that this approach has a promising potential for automated ocular cancer classification with an appropriate transfer learning model.

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An innovation prediction of DNA damage of melanoma skin cancer patients using deep learning

An innovation prediction of DNA damage of melanoma skin canc...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rakesh Ramakrishnan Mehmood Ali Mohammed Murtuza Ali Mohammed Vazeer Ali Mohammed J. Logeshwaran Maheswaran S Department of Information Technology University of The Cumberlands Union City CA USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer & Information System Lewis University Illinois USA Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Kongu Engineering College Erode Tamil Nadu India

There are now a variety of intriguing options for the study of genetic data thanks to recent developments in artificial neural networks and deep learning. In this study, we use a deep learning-based prediction model to find possible DNA damage in individuals with melanoma skin cancer. We create a convolutional neural network (CNN) model to forecast the DNA damage susceptibility of melanoma cancer cells using a publically available genome sequencing dataset. This model preprocesses the genomic data, extracts features, and categorises them. Comparing the results of our CNN model with those of a traditional logistic regression model, we find that our CNN reported superior performance in identifying differences between healthy and cancerous samples with an accuracy of nearly 96%. The model can be used to augment the standard clinical diagnosis of melanoma, which only uses visual assessment and histology. By intervening sooner, clinicians can put forward more personalized and informed plans of care and surveillance for each melanoma patient, reducing medical costs and improving the quality of patient diagnosis.

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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Smart electronics and Communication (ICOSEC), International Conference on

作者： Naga Venkata Ramakrishna Guduri S. Sethu R. Shalinirajan R. Reena Karthikeyan M G.S. Uthayakumar Electrical and Electronics Section Engineering Department University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamilnadu India

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%.

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Research on high-precision silicon-based integrated optical temperature sensor

Research on high-precision silicon-based integrated optical ...

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2021 Applied Optics and Photonics China: Optoelectronics and Nanophotonics, AOPC 2021

作者： Wang, Yimeng Shu, Haowen Han, Xiuyou School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510649996

Integrated optical temperature sensor nowadays has attracted extensive interests thanks to its merits of high accuracy, electromagnetic immunity, and compact size compared with the traditional solutions. So far, most of the state-of-art integrated optical temperature sensors work by spectrum detection of a resonant cavity, which has certain limitations in terms of response time and cost, and it is difficult to achieve high level integration of the overall system. To solve the above problems, in this paper we use silicon-based integrated micro-ring array technology for high-precision real-time temperature sensing. By constructing a monotonic relationship between the output light intensity of the micro-ring array with the variation of the temperature, rapid and high-precision temperature measurement could be realized. Depending on different practical scenarios, the system can be customized to adapt specific temperature measurement range and resolution. Through the optimized design, the system accuracy could be better than 60mK covering temperature range of 297K∼415K, with response time better than 20μs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Temperature measurement

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mohammad Ahmar Khan Pushpendra Kumar Verma Shivangi Ghaldiyal N. V. Balaji G.Arun Francis Swati Bula Patil Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman School of Computer Science and Applications IIMT University Meerut Department of Computer Applications Invertis University Bareilly Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India IT Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab’s gift to the. We provide the whole text of the news that have already been applied based on human evaluation of the articles’ authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study.

关键词： Deep learning Accuracy Text categorization Training data Big Data Natural language processing Reliability Labeling Fake news

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