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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024 Neural information processing systems foundation. All rights reserved.

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Optihybrid: a modified firebug swarm optimization algorithm for optimal sizing of hybrid renewable power system

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Neural Computing and Applications 2024年第34期36卷 21517-21543页

作者： Abd El-Sattar, Hoda Kamel, Salah Hashim, Fatma A. Sabbeh, Sahar F. Luxor Higher Institute of Engineering and Technology Luxor85834 Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt Faculty of Engineering Helwan University Cairo Egypt MEU Research Unit Middle East University Amman1183 Jordan College of Computer Science and Engineering University of Jeddah Jeddah21493 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha13518 Egypt

In areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms. © The Author(s) 2024.

关键词： Hybrid power

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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Dynamic Feature Selection Algorithm for Enhanced Time-Series Energy Consumption Forecasting

Dynamic Feature Selection Algorithm for Enhanced Time-Series...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Saranya, K. Sankaradass, Veeramalai Antony Suresh, V. Manimozhi, G. Panimalar Institute of Technology Department of Artificial Intelligence and Data Science Chennai602 123 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600 069 India St. Joseph College of Engineering Department of Information Technology Chennai602 117 India

ISBN: (纸本)9798331541170

Accurate power intake projecting is essential for enhancing power source administration, lowering power expenses and also preparing for future power demands. Because of solar power's intermittent nature coupled with minimal ability to handle solar problems grid drivers worldwide have a hard time to efficiently incorporate solar power right into the system to meet present need. This requires a thorough service to properly anticipate as well as take care of solar energy radiation as well as generation, attending to the progressing characteristics of supply as well as need on the grid. The purpose of the job is to produce a artificial intelligence (AI) design for exact forecast of solar energy radiation coupled with generation, along with improved power intake projecting by utilizing vibrant attributes to enhance flexibility and also precision. The method includes numerous artificial intelligence formulas from monitored set understanding coupled with deep discovering groups for maximized projecting. The datasets for radiation as well as generation have actually been sourced from Kaggle's system. The very best precision has actually been acquired from the Linear Regression for radiation is 97.75 together with the Random Forest Regression for generation is *** employing SARIMAX, the system aims to capture the complex temporal dynamics inherent in energy consumption data, thereby enabling precise forecasting of future consumption patterns. The optimized forecasting model facilitates improved decision-making for solar power generation scheduling, resource allocation, and grid integration, ultimately contributing to the advancement of sustainable energy systems. © 2024 IEEE.

关键词： Solar power generation

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Comment Generator for Java using Deep Learning

Comment Generator for Java using Deep Learning

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Saranya, S. Devi, S. Suruthi, R.M. Naveenkumar, M. Sethubharathi, B. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore India Coimbatore Institute of Technology Department of Information Technology Coimbatore India Coimbatore Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350335835

Working on a project is simple until we are the only ones involved and know all of the modules and processes. However, the issue occurs when a fresh developer works on your project after a lengthy period of time or when you work in a team. If you don't provide the appropriate comments, reading and comprehending the code becomes significantly more difficult. We can't tell what the code is doing just by reading it. As a result, providing useful comments is critical to understanding the code and working on it further. We present a technique to generate automated comments using a deep learning model that will take your whole block of code as input and produce a relevant remark. With Java, the suggested model achieves an accuracy of roughly 75%. This may differ in other languages such as Python, Go, and Javascript. This may be utilized in a large project by a person or a firm to easily collect opinions, reducing human labor and time to a large amount. The model has a BLEU score of 65 percent. © 2023 IEEE.

关键词： Project management

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Machine Vision-Driven Semantic Segmentation for Autonomous Navigation 3

Machine Vision-Driven Semantic Segmentation for Autonomous N...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Bala Krishna, N. Lokesh, K. Vali, Dudekula Azmeer Mahathi, Kanna Kumar, Koramatla Siva Padmini, Gudisirayula Andhra Pradesh Tirupati India Mother Theresa Institute of Engineering and Technology Department of Artificial Intelligence & Data Science Andhra Pradesh Palamaner India Department of Cse Andhra Pradesh Tirupati India

ISBN: (纸本)9798350375190

One of the main roles played by real time image segmentation is to enhance and catalyse self driving cars that can accurately sense their surroundings due to in terms of proper functioning. The new model was proposed with a completely new image segmentation paradigm for enabling self-driving in real time. The deployment of deep learning techniques, accurate real-time object segmentation through a fusion of fully convolutional network (FCN) with multilevel pyramid analysis. FCN uses the input image for segmentation, and in return creates a map-based on pixel-wise classification from which we can get more detailed information about different objects present in the scene. This approach creates a very large FCN network that is combined with a multiscale pyramid architecture that process an image in multiple stages effectively collecting complex surroundings information to improve performance. The proposed method is then experimented on different data with a particular focus on challenging to adopt such infrastructure in order to achieve modern day results in terms of accuracy and real time performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Using Convolutional Neural Networks for Enhanced Pneumonia Detection via Chest X-Rays

Using Convolutional Neural Networks for Enhanced Pneumonia D...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Babar, Waqar Ali, Raja Hashim Faheem, Alishba Mansoor, Syed Ahmed Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

As a deadly lung disease, pneumonia remains a leading cause of child mortality under five years old. Machine learning, especially deep learning have played a significant role in the improved performance for detection and identification of various diseases in the field of healthcare. Neural networks, especially the recent development of newer architectures, have revolutionized the object identification, and classification applications in clinical diagnosis of various diseases. This study presents the application of Convolutional Neural Networks (CNN) for the timely and accurate detection of pneumonia using chest X-rays, a development with considerable potential in aiding clinical diagnosis. The research deployed dropout regularization in model design to mitigate overfitting and relied on recall and F1 scores for thorough model evaluation. Although comparable studies achieved higher overall accuracy, our models registered a recall rate of 98%, crucial in reducing false negatives and enhancing patient safety. This suggests the potential of our CNN model as a vital tool for healthcare professionals in early pneumonia detection in children and adults, with the capacity to process a high volume of X-ray images rapidly and accurately. The successful model construction was enabled through various parameter tuning techniques, thus enhancing patient care efficiency and the potential to decrease mortality rates. © 2024 IEEE.

关键词： Convolutional neural networks

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 IEEE.

关键词： Object detection

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Bank Note Authentication using Deep Learning

Bank Note Authentication using Deep Learning

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mian, Reiaan Mazhar Khan, Sohaib Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

The swift progression of technology has resulted in a surge in digital transactions, necessitating robust authentication mechanisms to avert fraudulent activities. Because genuine and counterfeit banknotes seem so identical, it is difficult, time-consuming, and error-prone for humans to determine which one is authentic. Counterfeit notes are manufactured at an alarmingly high rate, which means that systems for authenticating bank notes are essential to ensure counterfeit notes are detected and verified quickly and in due time. Such measures guarantee multiple benefits, including safe transactions, uphold the integrity of financial institutions, and guardianship against fake currency notes in the market. In this work, we show that automated solutions may be deployed to improve the accuracy and efficiency of procedures for banknote authentication. Currently, there is a lack of effective and precise models, which are especially trained for verifying or detecting bank note authentication, despite presence of an extensive research in the fields of artificial intelligence and machine learning. Our study seeks to close this gap by creating and implementing a deep learning model that can accurately identify and differentiate between real and fake banknotes. In the study, we noted that the suggested model outperformed conventional techniques with a notable degree of accuracy with the use of an extensive dataset. The model’s potential for practical implementation in banking and financial establishments is shown by its exceptional accuracy and efficiency in detecting fake banknotes. The high accuracy of the model demonstrates that deep learning may be used successfully to improve security protocols, opening the door for more developments in financial technology and otehr allied fields. © 2024 IEEE.

关键词： Deep learning

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