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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

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Energy Efficiency Optimization in Massive MIMO Systems with Low-Resolution ADCs 2

Energy Efficiency Optimization in Massive MIMO Systems with ...

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2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Musa, Abdulwaheed Adebayo, Abdullateef Ola Peace Balogun, Oluwasijibomi Kwara State University Department of Electrical and Computer Engineering Malete Nigeria Kwara State University Centre for Artificial Intelligence and Machine Learning Systems Malete Nigeria University of Johannesburg Institute for Intelligent Systems South Africa

ISBN: (纸本)9798350358834

Massive multiple-input multiple-output (MIMO) is a promising 5G technology, but the high energy consumption from numerous radio components is a key challenge. Prior work has explored higher than 4-bit ADCs, but very low 1-2 bit remain uninvestigated. This paper provides an in-depth examination of the resolution versus power savings and signal quality degradation trade-offs with 1-3-bit analog-to-digital converters (ADCs) in Massive MIMO systems. Mathematical modeling and simulations quantify the exponential reduction in power consumption along with losses in key performance metrics like signal-to-interference-plus-noise ratio (SINR), throughput, and spectral efficiency with lower ADC resolutions stemming from increased quantization noise. Advanced linear detection techniques are shown to effectively recover the signal quality degradation. The analysis provides valuable insights on jointly optimizing ADC configuration and signal processing to maximize overall system efficiency. Energy savings of over 50% are demonstrated with 1-bit ADCs along with the ability to maintain acceptable performance metrics using linear detectors. The practical implementation feasibility is also discussed. This work enables ultra-low-resolution ADC deployments in energy-efficient Massive MIMO systems. © 2023 IEEE.

关键词： Energy efficiency

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Balancing Privacy and Performance for Private Federated learning Algorithms

Balancing Privacy and Performance for Private Federated Lear...

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Federated learning Technologies and Applications (FLTA), International Conference on

作者： Xiangjian Hou Sarit Khirirat Mohammad Yaqub Samuel Horváth Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

Federated learning (FL) is a distributed machine learning (ML) framework where multiple clients collaborate to train a model without exposing their private data. FL involves cycles of local computations and bi-directional communications between the clients and server. To bolster data security during this process, FL algorithms frequently employ a differential privacy (DP) mechanism that introduces noise into each client's model updates before sharing. However, while enhancing privacy, the DP mechanism often hampers convergence performance. In this paper, we posit that an optimal balance exists between the number of local steps and communication rounds, one that maximizes the convergence performance within a given privacy budget. Specifically, we prove the optimal number of local steps and communication rounds that enhance the convergence bounds of the DP version of the ScaffNew algorithm. Our findings reveal a direct correlation between the optimal number of local steps, communication rounds, and a set of variables, e.g., the DP privacy budget and other problem parameters, specifically in the context of strongly convex optimization. Furthermore, we provide empirical evidence to validate our theoretical findings.

关键词： Privacy machine learning algorithms Federated learning Computational modeling Noise Distributed databases Data models Convex functions Servers Convergence

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Streamlining Attendance with Fast Face Recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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

作者： Sundar, Koushik Ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： Database systems

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artificial Electromagnetic Medium Usages in the Energy Sector for the Purposes of Transmission

Artificial Electromagnetic Medium Usages in the Energy Secto...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bargavi, Manju Chandra, Subhash Saxena, Nishant Department of Computer Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India Tula's Institute Department of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350357769

We provide a thorough investigation of the field of metamaterials in order and meta surfaces with particular attention to its uses in wireless energy extraction and wireless power transfer (WPT). In WPT, the electromagnetic field path from source to receiver is divided by radiative technologies, which include both near- and far-field methods, and nonradiative near-field technologies. We evaluate a wide range of designs, comparing and contrasting their many aspects. Our research shows that meta material and meta surfaces are essential for increasing the effectiveness and operating diversity of WPT systems. These novel materials may reduce sensitivity to incoming wave angles and polarizations, increasing the energy conversion effectiveness in wireless energy harvesters. A vital component of both WPT und WEH is the rectenna. Our results demonstrate the advantages of rectennas based on metamaterials, demonstrating their capacity to attain better conversion efficiencies from RF to DC. These prospects highlight the dynamic character of this area, ranging from researching innovative applications to enhancing current solutions. With the advent of wireless energy collecting and transmission, metamaterials play an increasingly important role in influencing the development of efficient and sustainable technologies. © 2023 IEEE.

关键词： Antennas meta materials meta surfaces rectennas wireless energy harvesting and wireless power transfer

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Detection of Synthesized Satellite Images Using Deep Neural Networks

Detection of Synthesized Satellite Images Using Deep Neural ...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Liao, Wen-Hung Chang, Yi-Shan Wu, Yi-Chieh National Chengchi University Dept. of Computer Science Taipei Taiwan Artificial Intelligence and E-learning Center National Chengchi University Taipei Taiwan

ISBN: (纸本)9781665453486

The technology of generative adversarial networks (GAN) is constantly evolving, and synthesized images can no longer be accurately distinguished by the human eyes alone. GAN has been applied to the analysis of satellite images, mostly for the purpose of data augmentation. Recently, however, we have seen a twist in its usage. In information warfare, GAN has been used to create fake satellite images or modify the image content by putting fake bridges, buildings and clouds to mislead or conceal important intelligence. To address the increasing counterfeit cases in satellite images, the goal of this research is to develop algorithms that can classify fake remote sensing images robustly and efficiently. There exist many techniques to synthesize or manipulate the content of satellite images. In this paper, we focus on the case when the entire image is forged. Three satellite image synthesis methods, including ProGAN, cGAN and CycleGAN will be investigated. The effect of image pre-processing such as histogram equalization and bilateral filter will also be evaluated. Experiments show that satellite images generated by different GANs can be easily identified by individually trained models. The performance degraded when model trained with one type of GAN samples is employed to determine the originality of images synthesized with other types of GANs. Additionally, when histogram equalization is applied to the images, the detection model fails to distinguish its authenticity. A four-class universal classification model is proposed to address this issue. An overall accuracy of over 99% has been achieved even when pre-processing has been applied. © 2023 IEEE.

关键词： Generative adversarial networks

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Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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Grammar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Geetha, M. Vijaya, K. Subramanian, K.M. Rajesh Kanna, P. Rajeshkumar, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from Instruments Grammar Error Correction Natural Language Processing Universal Language Model Fine-Tuning

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ParkinSense: A Tri-Sensory AI-Powered Framework for Early Detection of Parkinson’s Disease 9th

ParkinSense: A Tri-Sensory AI-Powered Framework for Early De...

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9th International Conference on Information, Communication and Computing Technology, ICICCT 2024

作者： Akshaya, M. Joy, Emmanuel Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9783031724824

Parkinson’s disease (PD) is a brain dysfunction condition that affects thousands of people across the globe. Early detection of Parkinson’s disease is crucial for effective treatment and control of symptoms. This study employs a multi-modal methodology coded as ParkinSense that combines spiral drawings, gait analysis, and Magnetic Resonance Imaging (MRI) analysis for timely Parkinson’s disease identification. MRI analysis provides insight into structural and functional changes in the brain, revealing potential anomalies. GAIT analysis assesses alterations in walking patterns, such as reduced arm swing and slow gait velocity. Spiral drawings, serve as a surrogate for motor dexterity and fine motor control, both of which are compromised in Parkinson’s disease. In this study, data from Parkinson’s patients and healthy candidates are collected and analyzed using advanced machine-learning techniques to create models for accurate disease prediction and classification. Specifically, the gait analysis is conducted using a Support Vector machine and XGBoost algorithm. The spiral drawings are analyzed using a Convolutional Neural Network. The MRI data is processed using a MobileNet-based neural network. These algorithms are integrated to create models for accurate disease prediction and classification. The results demonstrate that this multimodal technique is equipped to identify Parkinson’s disease with an accuracy rate of more than 90%. Early detection using speech analysis, gait analysis, and spiral drawings can lead to earlier intervention and treatment, resulting in a better excellence of life for those affected. This study adds to the expanding body of knowledge about non-invasive, cost-effective approaches for the timely identification of Parkinson’s disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Feature Selection Based Naïve Bayes Algorithm for Twitter Sentiment Analysis 4

Feature Selection Based Naïve Bayes Algorithm for Twitter S...

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Aruna, T.M. Asha, K. Divyaraj, G.N. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India REVA University Department of Computer Science and Engineering Bengaluru India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

A intriguing areas of study now is Twitter sentiment investigation. It fuses the data mining methods used to create such systems with natural language processing techniques. The majority of currently available methods for analyzing Twitter sentiment do not do well when presented with messages that are both brief and ambiguous, since this is the only kind of information they take into account. Better classification outcomes are guaranteed if the right collection of features is used to identify emotion in online textual material. The computational difficulty of doing optimal feature selection drives the need for the development of creative approaches to enhancing classifier performance. In this work, an effective method for analyzing Twitter user sentiment was presented. A machine learning model was developed by the suggested method to identify good and negative tweets. Characteristics are extracted after pre-processing, and it's possible that include irrelevant features can lower classification accuracy. For this reason, the model implemented the Vortex Search Algorithm (VSA) to choose the best characteristics and discard the rest. The final tweets categorization is done using the Naive Bayes (NB) algorithm. A total of four Twitter datasets are used for the studies, each measuring a unique set of parameters and made accessible to the public. Marketing, detecting political polarization, and product reviews are just a few of the many areas that may benefit from the suggested system's ability to gauge user sentiment based on tweets. © 2022 IEEE.

关键词： Sentiment analysis

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PLT-Net: A 2-Layer SP-Network using a Modified Playfair Cipher and the Logistic-Tent Map 13

PLT-Net: A 2-Layer SP-Network using a Modified Playfair Ciph...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Maitra, Anurag Pal, Anirban Karmakar, Raja Department of CSE - Artificial Intelligence and Machine Learning Techno Main Salt Lake Kolkata700091 India Department of Computer Science and Engineering Techno International New Town Kolkata700156 India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The Playfair cipher is a classic digraph substitution cipher. Over the years, it has been modified in many ways to increase its security. Most of its modifications are basically substitution ciphers with limited confusion and diffusion, and are vulnerable to chosen-plaintext attacks, ciphertext-only attacks, and even frequency analysis. In this paper, we introduce a modification of the Playfair cipher, Playfair Logistic-Tent Network (PLT-Net), that uses two 5X19 key tables simultaneously to randomly encrypt the text. The key tables consist of all printable characters. This modification has been used in combination with the chaotic logistic-tent map as a permutation cipher, to create a 2-layer substitution-permutation network with better security, where the encrypted text is scrambled in each layer to break the digraphs. The combination of a substitution cipher and a permutation cipher has produced an algorithm that is free from the drawbacks of both. Cryptanalysis has been performed on the PLT-Net to showcase its strengths. It has better confusion and diffusion, as changing a single character, either in the key or in the plaintext, completely changes the ciphertext. © 2022 IEEE.

关键词： Directed graphs

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