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Psi-zeta Approach for Assessing Network Efficiency in Log-Normal Fading and Spectrum Sharing Interference Environments 3

Psi-zeta Approach for Assessing Network Efficiency in Log-No...

引用

3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Narooka, Preeti Verma, Shekhar Dwivedi, Abhishek Kaushik, Keshav Singh, Vineeta Gaur, Priyanka School of Computer and Engineering Manipal University Rajasthan Jaipur India Department of Computer Applications School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kalyanpur Kanpur India Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kalyanpur Kanpur India Dept. of Computer Applications Chandigarh Group of Colleges Jhanjheri Mohali India

ISBN: (纸本)9798331543891

The paper introduced the Psi-zeta computation of network efficiency under a scenario of log-normal fading and spectrum sharing interferences. This model is aimed to improve different performance metrics such as throughput, Signal-to-Interference-plus-Noise-Ratio (SINR), energy efficiency and finally outage probability also reducing. Simulations demonstrate that when evaluating the overall performance, Psi-zeta is superior to traditional methods by managing interference and resource allocation agilely. The results, especially under high load and heavy interference conditions, exhibit remarkable enhancements in network performance. The strong modifiability and expandability of psi-zeta show that it can be a good candidate for improving network efficiency in future wireless networks like 6G, where parameters like performance, reliability and energy efficiency are the key factors. This work has significant potential to enable the next-generation communication systems. © 2025 IEEE.

关键词： Electric signal systems

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Implications of continual learning for lesion of the skin categorizing and detection

Implications of continual learning for lesion of the skin ca...

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Advances in Computational Intelligence and Communication (ICACIC), International Conference on

作者： V.Auxilia Osvin Nancy V Rajasekar Meenakshi S Arya Dept. of Computer Science and Engineering College of Engineering and Technology SRM Institute of science and Technology Chennai India Institute of Transportation IOWA state University United States

Development of techniques that can gradually learn from new samples is now important due to the growing number of data. Training a network with new data poses challenges, including the phenomenon known as "catastrophic forgetting." This occurs when a network's performance on previously learned data drops as it tries to learn from new instances. To address the issue of catastrophic forgetting, the article includes a powerful approach known as experience replay. Investigations are conducted on different datasets to analyze the impact of catastrophic forgetting. The technique is compared against various approaches, including individual training, mutual training, and fine-tuning. When compared to the fine-tuning approach, the model using experience replay retains knowledge from the previous dataset, leading to a 13% increase in accuracy.

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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A Real-Time P2P Bot Host Detection in a Large-Scale Network Using Statistical Network Traffic Features and Apache Spark Streaming Platform

A Real-Time P2P Bot Host Detection in a Large-Scale Network ...

引用

International Conference for Convergence of technology (I2CT)

作者： S. Saravanan G. Prakash B. Uma Maheswari Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Nowadays, Peer-to-Peer (P2P) bots play a significant role in launching attacks such as phishing, distributed denial-of-service (DDoS), email spam, click fraud, cryptocurrency mining, etc. The analysis of statistical network traffic features of hosts is one of the commonly used methods to detect P2P bots. Modern P2P bot detection systems need to extract features from massive streaming network traffic as the size of the Internet keeps increasing every day. However, traditional detection systems have trouble detecting bots in real-time in large-scale networks as they are not implemented on big data streaming platforms. Hence, this work proposes a network flow-based P2P bot detection system implemented on Apache Spark Structured Streaming Platform to detect P2P bots in real time by analyzing massive streaming network traffic data generated from large-scale networks. Such detection of P2P bots is based on statistical network traffic features: destination diversity ratio, control packets ratio, and total source bytes sent in a flow. There are two components in the proposed system: the first component detects potential P2P hosts using the Destination Diversity Ratio (DDR), and the second component finds out P2P bot hosts from the P2P hosts identified by the first component. Furthermore, the performance of the detection components depends on the time window at which statistical features are extracted. Hence, this work also conducted experiments to study the effect of different time windows on detection components. The proposed system is evaluated using real-world datasets and achieves a True Positive Rate (TPR) of 99.87%.

关键词： Botnet Unsolicited e-mail Telecommunication traffic Cluster computing Big Data Chatbots Feature extraction

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Federated Digital Twin

Federated Digital Twin

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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A New Compensation Topology For Inductive Power Transfer (IPT) Systems

A New Compensation Topology For Inductive Power Transfer (IP...

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Annual Conference of Industrial Electronics Society

作者： You-Chun Huang Udaya Madawala Craig A. Baguley Yao-Ching Hsieh Dept. of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan Dept. of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Dept. of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand

This paper presents a new IPT compensation topology that can be operated at two different frequencies to form either primary series and secondary parallel (S-P) or primary parallel and secondary parallel (P-P) compensation. Model are presented to demonstrate how this transformation between S-P and P-P compensations with dual frequency control. The simulated results of a 1 kW IPT system have verified that the proposed system can remain output power with phase-shift modulation (PSM) and allows for efficient power transfer despite large pad misalignments by operating at different compensation.

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Psi-zeta Approach for Assessing Network Efficiency in Log-Normal Fading and Spectrum Sharing Interference Environments

Psi-zeta Approach for Assessing Network Efficiency in Log-No...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Preeti Narooka Shekhar Verma Abhishek Dwivedi Keshav Kaushik Vineeta Singh Priyanka Gaur Artificial Intelligence and Machine Learning School of Computer and Engineering Manipal University Jaipur Rajasthan India Department of Computer Applications School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kanpur India Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Chhatrapati Shahu ji Maharaj University Kanpur India Dept. of Computer Applications Chandigarh Group of Colleges Jhanjheri Mohali India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

关键词： Fading channels Wireless networks Interference Probability Throughput Energy efficiency Power system reliability Reliability Next generation networking Signal to noise ratio

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Robust Speech Analysis Framework Using CNN 3

Robust Speech Analysis Framework Using CNN

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rupasinghe, Lakmal Alahendra, A.M.A.T.N. Ranathunge, R.A.D.O. Perera, P.S. Kulathunge, N.Y. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice is the main component of human communication and learning about and recognizing somebody's behavior. By listening to people's voices, humans can recognize a person's identity, speech fluency, accent, emotions, and stress level. It is difficult to understand what the speaker is saying when Speech fluency is poor. It varies from person to person. With the help of specific information in a person's voice, we can recognize human emotion, stress level, and identity. Every person has a unique vocal feature that facilitates recognizing them from others. This proposed framework is developed to identify a person's identity, emotions, fluency in speaking, and stress level of the speaker using their voice. The proposed framework is developed using machine learning techniques, and deep learning algorithms are highlighted in this study. Convolution Neural Network (CNN) is the used deep learning algorithm, and Fast Fourier transform (FFT), (MFCC), and Random Forest are machine learning techniques. The proposed AI-based framework provides comparatively accurate results in a user-friendly way. © 2021 IEEE.

关键词： Stress analysis

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos School of Computer Science University of Birmingham Birmingham UK Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

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Auto Training an AI for Detecting Plant Disease Using Twitter Data Annexed with a Plant Anthology 3

Auto Training an AI for Detecting Plant Disease Using Twitte...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Vasanthan, Niroshini Shimran, Mohamed Ahkam, Ahmadu Ishak, Ishma Silva, Chamari Kuruppu, Thilmi Anuththara Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Agricultural productivity plays a vital role in contributing to a nation's economy. Farmers nowadays are concerned due to disease persistence in crops and plants, and it also affects the economy indirectly, so it is important to come up with a solution to detect plant diseases and educate the farmers about the solutions to retaliate against the diseases. Proper care is mandatory to safeguard the quality of plants. The existing traditional methods consume a massive amount of time and resources hence, it's costly. Due to the importance of continuous monitoring, it seems impractical for a farmer to implement the traditional methods on large scale. The Traditional systems which are used lack the ability to identify diseases out of their predefined scope. As a solution, we came up with an auto-learning system that identifies new plant diseases and provides remedies. This paper showcases the image processing techniques to detect plant diseases, Auto ML techniques to create new models for plants and corresponding diseases, Diseases are identified using image processing, Remedies are extracted for the given plant diseases using unstructured data from web data crawling. The business intelligence model uses NLP to provide ideas about the trending plants and plant-related diseases are also discussed in this paper. © 2021 IEEE.

关键词： Image classification

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