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

作者： Sudhamsu, Gadug Mishra, Awakash Kale, Kiran Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

With the ability to communicate between machines and people, the Tactile Internet (TI) represents a substantial advancement over the Internet of Things (IoT). TI wants to make it easier to develop real-time interactive applications in a variety of fields, including as engineering, social engagement, and business. The future 5th Generation (5G) technology is focused on offering Ultra-Reliable Low Latency Communication (URLLC) services to satisfy the demanding needs of TI applications. According to 3GPP specifications, URLLC may transmit a single 32-byte packet with a latency of less than one millisecond and achieve 99.99% dependability. In order to accomplish these objectives, the 3GPP presents 5G New Radio (5G NR), a flexible orthogonal frequency division multiplexing (OFDM) approach, as a new radio access technology (RAT). With the introduction of this new RAT, it is clear that we need next-generation solutions, especially ones that include network intelligence. To fulfil the URLLC needs of 5G NR, machine learning (ML) methods are anticipated to be crucial in building intelligent network resource distribution algorithms. This review analyses the possible use of Federated reinforcement learned, an ML approach, to satisfy the needs of 5G NR URLLC and lists the pertinent URLLC accomplishments. Plane channel entry techniques that support URLLC in 5G NR for TI workloads are covered in great detail in this study. It also pinpoints seven crucial upcoming situations where FRL might function as a possible facilitator for URLLC in 5G NR. © 2023 IEEE.

关键词： 3GPP specifications 5G Federated reinforcement learning latency and reliability machine learning network architecture New Radio Tactile Internet URLLC

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Age and Gender Classification Using Backpropagation and Bagging Algorithms

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computers, Materials & Continua 2023年第2期74卷 3045-3062页

作者： Ammar Almomani Mohammed Alweshah Waleed Alomoush Mohammad Alauthman Aseel Jabai Anwar Abbass Ghufran Hamad Meral Abdalla Brij B.Gupta Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT-department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Prince Abdullah Ben Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversityAl-SaltJordan School of Information Technology Skyline University CollegeSharjah P.O.Box 1797United Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science and Information Engineering Asia UniversityTaizhongTaiwan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security *** main aim of the research is to develop a system able to predicate and classify gender,age,and ***,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is *** and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender *** has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification *** has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

关键词： Classify voice gender accent age bagging algorithms back propagation algorithms AI classifiers

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Translational Deep Learning-Based Diagnostics: Enhancing Pathology With AI

Translational Deep Learning-Based Diagnostics: Enhancing Pat...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Ashwini, B. Sinha, Deepak Kumar Chaudhary, Chetan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Transformative innovation in healthcare is led by the constantly changing field of deep learning techniques, especially designed for cellular imaging in diagnostic pathology. This paper provides a thorough examination of several deep learning approaches in the field of pathology, highlighting new uses and tackling important issues that are essential to the field's progress. The successful deployment of AI in clinical settings depends on the seamless integration of these apps into processes in digital pathology. This all-inclusive diagnostic method not only simplifies processes but also provides pathologists with cutting-edge instruments that accelerate work, improve diagnosis accuracy, and lower mistakes. The US has recently approved a limited number of manufacturers' digital pathology products for diagnostic usage, which offers a strong basis for the creation of useful AI solutions. Researchers, physicians, business executives, government agencies, and patient advocacy organizations working together will make it much easier for these cutting-edge technologies to spread and become widely used by medical professionals. These technologies usher in a new age in healthcare, distinguished by their exceptional performance, faster processing rates, cost effectiveness, increased accuracy, and strict safety regulations. AI and computational pathology will continue to develop in harmony, opening the door to a new age of improved healthcare delivery. © 2023 IEEE.

关键词： Artificial Intelligence Cellular Imaging Deep Learning Diagnostic Imaging and Machine Intelligence Diagnostics Digital Pathology Healthcare Pathology

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Fast Learning Network Algorithm for Voice Pathology Detection and Classification

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Multimedia Tools and Applications 2025年第17期84卷 18567-18598页

作者： Albadr, Musatafa Abbas Abbood Ayob, Masri Tiun, Sabrina AL-Dhief, Fahad Taha Al-Daweri, Muataz Salam Homod, Raad Z. Abbas, Ali Hashim Department of Petroleum Project Management College of Industrial Management of Oil and Gas Basrah University for Oil and Gas Al-Basrah Iraq Faculty of Information Science and Technology CAIT Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Department of Communication Engineering School of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor Malaysia Department of Information Systems College of Economics Management & Information Systems University of Nizwa Nizwa Oman Department of Oil and Gas Engineering Basrah University for Oil and Gas Al-Basrah Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

The utilisation of ML (Machine Learning) techniques in the detection of the VP (Voice Pathology) has recently gained a lot of consideration. However, these efforts still have several drawbacks such as: I) the accuracy rates of most previous works are still not promising and need more improvement. II) The majority of the previous works have concentrated on the task of VP detection only and disregarded the task of VP classification. III) Most of the previous works have been assessed based on a single voice data only like the vowel /a/, and the other vowels (/i/, and /u/) and sentences were disregarded. IV) The majority of the previous works performance were assessed utilising a limited evaluation metrics. Recently, one of the utmost effective ML techniques is FLN (Fast Learning Network), it is an efficient technique for data classification. However, the FLN classifier has not been implemented to the problem of VP detection and classification. Therefore, this research proposes the FLN classifier with MFCC (Mel-Frequency Cepstral Coefficient) features in order to enhance the accuracy of the VP detection and classification. The FLN classifier has the ability to a) solve both binary and multiclass classification issues, b) eliminate overfitting, as well as c) operate akin to a NN (Neural Network) structure while employing the principles of a kernel-based SVM (Support Vector Machine). In this research, the SVD (Saarbrucken Voice Database) dataset was utilised to assess the FLN classifier performance in the VP detection and classification. The assessment of the proposed FLN classifier was conducted based on two phases: the first phase includes all the voice samples of the SVD dataset with the sentences and vowels (i.e., /a/, /i/, and /u/) which are pronounced in neutral, high, and low pitches. While the second phase uses the voice samples of the utmost common 3 pathology types (i.e., paralysis, polyp, and cyst) based on the vowel /a/ that is pronounced in neutral pitch. The

关键词： Classification (of information)

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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Nodal Transmission in 5G Wireless Networking

Nodal Transmission in 5G Wireless Networking

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

作者： Agrawal, Trapty Kakati, Pallabi Das, Sunanda Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station. © 2023 IEEE.

关键词： 5G Wireless Networking Communication Networks Data Transmission Interference Analysis Network Security Nodal Transmission NS3 Network Simulator Signal Quality And Distribution Algorithms Simulation Modeling

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Beyond The Internet of Things: Imagining IoT's Structure and Potential Routes

Beyond The Internet of Things: Imagining IoT's Structure and...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Varalakshmi, K.R. Mahesh, T.R. Singh, Akhilendra Pratap Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

wireless sensor system technology has made it possible for ubiquitous sensing, which affects many aspects of contemporary life. Indicators of environmental health, from sensitive ecologies as well as biodiversity to urban settings, may now be measured, inferred, and understood. The Internet of Things is enabled by the extensive utilization of these devices in a network that facilitates communication and action. In this network, the actuators and sensors smoothly interact with our environment as data is exchanged across platforms to provide a unified operational view. Near field communication (NFC) is the upcoming technology that will enable the transformation of the worldwide web into a fully integrated Future Internet. This technology is being advanced through the adoption of variousenabling device methods, such as tags, audience members, encased sense and actuators nodes, and nodes for near field interaction. The demand for data-on-demand, using complex and straightforward queries, increases significantly as studies transition from web to web2 to web3. This study presents a cloud-centric vision for the worldwideimplementation of the Internet of Things. The future of IoT research is expected to be influenced by many significant technology enablers and their corresponding application domains, which are emphasized. A cloud architecture based on the interplay of public as well as private clouds and employing Aneka is shown. Study conducted elaborate on the need of unity, the global web, and distributed processing in our last section of our IoT vision for the community of technical researchers. © 2023 IEEE.

关键词： Cloud-centric Internet of Things Data storage and analytics Internet of Things (IoT) New protocols Participatory Sensing Quality of Service (QoS) Radio Frequency Identification (RFID) User-cantered cloud-based paradigm Visualization Wireless Sensor Networks

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Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

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computers, Materials & Continua 2021年第4期67卷 1137-1152页

作者： Durr-e-Nayab Mohammad Haseeb Zafar Mohammed Basheri Faculty of Electrical and Computer Engineering University of Engineering and TechnologyPeshawar25120Pakistan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing *** mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and *** work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests *** search request is classied based on ERS attributes and then processed for routing while monitoring the node *** algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the *** network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS *** aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with *** simulations are carried out for network scalability,node speed and network terrain *** results show that the proposed models attain evident performance enhancement.

关键词： Expanding ring search mobile ad hoc networks multi hop wireless networks on-demand ad hoc networks per hop behavior quality of servi

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From Browsers to Bots Unveiling the Power of Bits in Bridging Business Gaps 6th

From Browsers to Bots Unveiling the Power of Bits in Bridgin...

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6th International Conference on information Systems and Management Science, ISMS 2023

作者： Baruah, Samannoy Beemkumar, N. Gantra, Amit Gautam, Anupam Kumar Department of Commerce and Management Assam Down Town University Assam Guwahati India Karnataka Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9783031664090

The plan laid forth here analyzes in detail the effect of the book "From Browsers to Bots: Unveiling the Power of Bits in Bridging Business Gaps". To conduct this investigation, an algorithmic structure is used. The method takes into account scalability, online sentiment research, sales and revenue analysis, and customer involvement. The purpose of this research is to provide a thorough and factual evaluation of the book’s impact on the business world by using algorithms to measure metrics like the Customer Engagement Score (CES), the Impact on Sales and Revenue Analysis, and the Online Sentiment Analysis. The results suggest that our method outperforms more conventional strategies in many key respects, and as a consequence provides a more comprehensive and precise assessment of the book’s impact. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Bot (Internet)

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma M.El-Ghamry Walid El-Shafai Mahmouad I.Abdalla Ghada M.El-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.Elfishawy Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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