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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

引用

2024 IEEE Students Conference on engineering and systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Real World Deployment of Dual Branch MRC Enhanced by Adaptive Algorithms LMS and RLS in 5G Networks to Improve SNR and QoS 4

Real World Deployment of Dual Branch MRC Enhanced by Adaptiv...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Maindola, Meenakshi Garg, Shobhit Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798350349900

This research is aimed at understanding the performance benefits of the use of adaptive algorithms in dual-branch MRC systems as a solution for developing 5G networks. We developed controlled simulations to determine the performance influences of the algorithms in SNR, QoS, BER, and throughput under different network conditions. Our results indicate that the algorithms significantly reduce BER safe power divergence that improves the reliability and performance of the communication platform. The adaptive systems in addition reveal a significantly impressive ability to maintain a higher rate of throughput that is necessary in the current networks due to the increasing volume of data consumed by different users. In conclusion, therefore, this research has showed that adaptive MRC techniques might be a highly-viable option toward enabling the development of 5G technology, provided a suitable method for boosting performance during uncertain communications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Implementation of Dynamic Spectrum Access Techniques in Cognitive Radio Networks under Generalized Fading Conditions 2

Implementation of Dynamic Spectrum Access Techniques in Cogn...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Garg, Shobhit Sharma, Amulya Devgan, Divyansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798331519056

This paper is organized as follows: Section II provides a background on our research work based on DSA in CRNs using CR under different fading conditions. c) Performance: We also study how effective adaptive algorithms along with the machine learning can prove in achieving higher spectrum sensing accuracy and decision making on run time. By conducting simulations and extensive performance evaluation, we show that the traditional solutions fall short when compared to our AI-based methodology in terms of accuracy especially under harsh fading conditions. Finally, sensitivity analysis highlights the control parameters that need to be submitted to further optimization for better system performance. With all above analysis, our results indicate that DSA can alleviate spectrum underutilization problem and enhance communication reliability in CRNs. Thus, with proper adaptation strategies and machine learning algorithms, the DSA systems provide an appealing opportunity to meet the continuously increasing requirements of bandwidth-consuming applications in current days wireless networks. The results of this research are helpful in providing effective value input to Advances in the understanding and design of robust, efficient spectrum management approaches for dynamic and diverse wireless settings. © 2024 IEEE.

关键词： Sensitivity analysis

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Machine Learning Approaches for Predictive Channel State Information in MIMO systems Using LSTM 2

Machine Learning Approaches for Predictive Channel State Inf...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Sharma, Amulya Gupta, Mridul Devgan, Divyansh Garg, Shobhit Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Management Studies Dehradun India

ISBN: (纸本)9798331519056

In this paper, we analyse machine learning models, including Long Short-Term Memory networks, to predict the CSI and evaluate their impact on adaptive transmission in MIMO systems. To achieve this objective, we use simulation to generate CSI data in a MIMO environment and compare the performance of ML models to traditional methods. Our simulation reveals using our dataset that LSTM models improve the SNR values significantly, which means that their predictions are more accurate. At the same time, these models require greater computational and other resources. In contrast, despite its relative inefficiency in comparison with LSTM, Decision Trees improve the SNR moderately while consuming fewer resources but needing a substantial amount of time. Analysis of the source tries to examine the balance between performance and resource usage may validate the use of ML-based methods, and especially LSTM, to predict the CSI advancedly, but the actual application must take constraints into account. © 2024 IEEE.

关键词： Contrastive Learning

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Software Effort Estimation using ANN (Back Propagation) 7

Software Effort Estimation using ANN (Back Propagation)

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ravi Kumar, B.N. Suresh, Yeresime Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9781665464086

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results. © 2023 IEEE.

关键词： Neural networks

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Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Demonstration of Effectiveness of Nativeness in Stepwise Lea...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Al-Mahmud Shimada, Kazutaka Kyushu Institute of Technology Dept. of Computer Science and Systems Engineering 680-4 Kawazu Iizuka 850-8502 Japan

ISBN: (纸本)9798350316001

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language;the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it 'nativeness.' The experimental result shows that nativeness has an important role in improving performance. © 2023 IEEE.

关键词： Sentiment analysis

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Cloud Cryptography: Mechanism of Different Encryption Standards 18

Cloud Cryptography: Mechanism of Different Encryption Standa...

引用

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

作者： Lanke, Ravikumar Md Zubair Rahman, A.M.J. Bhardwaj, Rajat Somashekhara Reddy, D. Jain, Parth Mahesh, T.R. Dept. of Computer Science and Engineering Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Aset Amity University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

The term 'cloud cryptography' describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architecture Benefits Cloud Computing Cryptography Encryption

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Secure and Efficient Key Management for Deduplicated Cloud Storage systems 16

Secure and Efficient Key Management for Deduplicated Cloud S...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Dave, Jay Kanodia, Anshul Srivastava, Raj Singh, Kushagra Hegde, Prithvi Desai, Hitaishi Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad Pilani India

ISBN: (纸本)9798350343212

Deduplication is a ubiquitous compression technique for cloud storage servers. It reduces storage and bandwidth requirements by avoiding duplicate copies of data. However, developing an encryption scheme for deduplication is a critical challenge because traditional encryption techniques are not compatible with deduplication. The existing state-of-the-art encryption schemes are at risk of brute-force attacks as they use deterministic techniques for key generation. Moreover, the user in the recent schemes suffers from storage overhead of deduplication metadata and encryption keys. In this paper, we introduce a secure and efficient key management scheme for deduplication. In our scheme, the user divides the file into blocks and encrypts each block using a random key. As a result, our scheme protects against brute-force attacks. In addition, the user securely shares the encryption keys on public storage servers with the aid of a private server. As a result, the proposed approach causes lower storage overhead. We implement our scheme in a real cloud scenario and evaluate the performance of our approach in terms of storage and computation overhead. © 2023 IEEE.

关键词： Cloud storage

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The Low-Complexity Timing Synchronization Circuits for IEEE 802.15.4a/z IR-UWB systems 6

The Low-Complexity Timing Synchronization Circuits for IEEE ...

引用

6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Wang, Kuan-Wei Shen, Chung-An Huang, Chin-Ya National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan

ISBN: (纸本)9798350378511

This paper presents the design and implementation of a low-complexity timing synchronization circuit for the IEEE 802.15.4a/z IR-UWB wireless communication standard. The circuit structure of a low-complexity correlator for complex ternary values is designed. Moreover, a two-stage synchronization approach is utilized for a more accurate sample alignment. Therefore, a balance between the performance of synchronization and hardware complexity is achieved. The proposed synchronization architecture is designed and implemented based on the Xilinx ZC706 FPGA platform. The experimental results show that the proposed design saves 74% of buffer and 87.4% of multiplier utilization. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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