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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Angami, Teisovi Tuithung, Themrichon Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India Department of Information Technology School of Engineering and Technology Nagaland University Lumami India

ISBN: (纸本)9789819757022

Tenyidie language belonging to the Tibeto-Burman family is one of the major languages of Nagaland in northeastern India. It is a low-resource, tonal, SOV, and a high agglutinative language. Stemming is an important Natural Language Processing (NLP) task wherein the stem or the root word is identified and extracted. To the best of the authors knowledge, there has been no work reported on corpus creation and stemming using deep learning for the Tenyidie language. In this paper, we build an annotated corpus of 5400 manually stemmed words in Tenyidie to apply deep learning to the stemming task. We applied state-of-the-art deep learning models, such as the Encoder-decoder models, and compared our results with the rule-based approach, which uses both affix stripping and root word matching methods. Our experimental result shows that the deep learning models outperform the rule-based approach. We achieved the highest accuracy of 85.55% with Encoder-decoder (BLSTM) with attention. We also provide an analysis of the overstemmed and understemmed words in our test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

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IEEE Transactions on engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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A Flow-Based Performance Evaluation on RYU SDN Controller

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Journal of The Institution of Engineers (India): Series B 2024年第2期105卷 203-215页

作者： Ram, Anil Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering NIT Jote Arunachal Pradesh Jote Itanagar791113 India Department of Computer Science and Information Technology Cotton University Pan Bazaar Assam Guwahati781001 India

Software-defined networking (SDN) is software-based networking technology which overcomes the complication tackled by traditional networking structures. The knowledge behind the SDN gives more flexibility to handle network as compared to traditional network since SDN detached the control plane (CP) from the data plane. As the SDN is a software-based technology, evaluation of performance is needed. The heart of SDN is SDN controller which is located in CP. SDN controller is accountable for the monitoring, decision making and investigation of factual time data traffic. Due to substantial rise of distributed real-time applications, there is a prerequisite for an efficient controller in networking sectors, data centers, academic and examination. As a result, it is critical to look at the presentation of a free-source specified controller in order to offer effective traffic routing, which will lead to better resource usage and improved network performance matrices. For network traffic analysis, the study proposes an execution of SDN structural design utilizing an RYU SDN controller. The present spadework assesses the performance of a bespoke network topology based on SDN architecture for device-to-device performance characteristics such as round trip time, bandwidth, and throughput. In the present default network topology for SDN, the simulation results show that our approach outperforms its competitors. © The Institution of Engineers (India) 2024.

关键词： Controllers

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Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

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computers, Materials & Continua 2023年第1期74卷 1413-1435页

作者： Aqsa Aqdus Rashid Amin Sadia Ramzan Sultan S.Alshamrani Abdullah Alshehri El-Sayed M.El-kenawy Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityAl BahaSaudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of *** traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be *** switch in SDN keeps track of forwarding information in a flow *** SDN switches must search the flow table for the flow rules that match the packets to handle the incoming *** to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding ***,the SDN must handle traffic from across the whole *** flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy *** the flow table is abused and overflowing,the usual regulations cannot be executed *** this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low *** study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)*** efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and *** findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G *** putting the proposed flow method

关键词： 5G networks software-defined networking(SDN) OpenFlow load balancing machine learning(ML) feed forward neural network(FFNN) k-means and decision tree(DT)

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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Performance Improvement: Modulation Recognition Scheme Using CVCNN-LSTM Method 1

Performance Improvement: Modulation Recognition Scheme Using...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Krishnamoorthy, Ramkumar Agarwal, Trapty Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The process for automatic modulation recognition (AMR) is important to industrial cognitive radio Models. Although they have made significant strides in modulation detection, state-of-the-art real-valued CNNs compromise model interpretability and signal phase integrity by treating complicated signal components as distinct actual inputs. The CVCNN-LSTM is a novel AMR technique that is presented in this research. In order to capitalize on the advantages associated with both networks while minimizing their drawbacks, this study intends to use CNNs along with a LSTM. This method improves recognition performance by strengthening the learning for the initial in-phase/quadrature information (I/Q) signal characteristics. The suggested network consists of one LSTM layer and many local features learning unit (LFLUs). Convolutional and average pooling layers, which are the main components of an LFLU, are essential for allowing hierarchical correlation extraction and local correlation learning. Researchers recommend employing end-to-end complex-valued CNN & LSTM for modulation recognition rather than dual-channel real-valued networks as in-phase/quadrature information naturally including complex-valued system. Researchers analyse complex-valued networks via experiments and show that this methodology outperforms the state-of-the-art techniques in AMR area. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimizing Underwater Acoustic Sensor Networks: Insights from Energy-Efficient, Reliable, and Void Issue-Based Routing Protocols 7

Optimizing Underwater Acoustic Sensor Networks: Insights fro...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Khan, Muhammad Umar Aamir, Muhammad Haider, Waleej Sir Syed University of Engineering and Technology Department of Telecommunication Engineering Pakistan Sir Syed University of Engineering and Technology Department of Computer Science and Information Technology Pakistan

ISBN: (纸本)9798331534271

The Underwater Acoustic Sensor Network (UASN) is based on the sensory elements that detect the environmental conditions of submerged regions and transmit the information through acoustic waves at the sink node placed on top of the water. All these sensory elements are battery-operated just like the Terrestrial Wireless Sensor Network (TWSN). However, these nodes are not stationary, unlike the nodes in TWSN. Thus, routing with limited energy and ever-changing topology decreases the reliability of the network and increases the chances of Void existence. This research paper focuses on the comparative analysis by critically reviewing some of the state-of-the-art routing protocols which are based on the effective usability of the limited energy resource, reliability of the network, and Void occurrence issues. An analytical method is employed to examine the strengths and shortcomings of existing routing protocols, with a specific emphasis on the identified difficulties. It provides a solid foundation related to UASN routing protocols which are primarily designed with a focus on resolving issues such as limited energy, low reliability, and high chances of void occurrence. The contribution of this comparative study helps the research partners from the academy and industry which are new to this field of study and would like to contribute. © 2024 IEEE.

关键词： Sensor nodes

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Enhancing Ground Network in UAV Assisted Wireless Communication 1

Enhancing Ground Network in UAV Assisted Wireless Communicat...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jebaraj, Solomon Yadav, Dhananjay Kumar Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The development of wireless technology for ground communications is being aided by the use of unmanned aerial vehicles (UAVs). It is believed that these technologies will continue to advance in the next years. Traditional methods of communication need elaborate, expensive, and laborintensive infrastructure, not to mention lengthy installation procedures. Instead of requiring expensive and time-consuming infrastructure construction, a UAV aided wireless network for communication guarantees a cheap wireless connection to devices. In comparison to High Altitude Platforms (HAPs) such as balloons with hot air or terrestrial systems, wireless communication systems that are incorporated with low level altitude UAVs provide quicker deployment, more mobility, easier re-configuration and according to expectations, improved channels for communication with a shorter line-of-sight (LOS) connectivity. However, unforeseen challenges impair the UAV to Ground system's performance and the reliability of the communication link between users and drones on the ground. Adaptive modulation and coding (AMC) are discussed in this letter as it pertains to Aerial-to-Ground (A2G) networks. Evaluating the downlink data rates and network capacity is our goal, along with reaching peak performance. Additionally, this research thoroughly evaluates the performance of terrestrial users connected to drones in different circumstances in terms of ratio of signal to noise, path loss, latency, and throughput. Furthermore, they investigated the A2G channels downlink link characteristics, which regulate the modulation and coding rate switching. © 2025 IEEE.

关键词： Drones

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Examining and Comparing: Performance of OFDM Utilised in 6G Communication 1

Examining and Comparing: Performance of OFDM Utilised in 6G ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Acharjya, Kalyan Suneetha, K. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798331518578

It is necessary to have a new and enhanced waveform system for transmission that is compatible with the band in order to accommodate the forthcoming 6G mobile network. Due to the fact that orthogonal frequency-division multiplexing (OFDM) is frequent wave form in 4Generation LTE & 5Generation new radio (NR) for enhanced mobile broadband or eMBB transmission of data on multicarrier, it is very probable that a more advanced version of OFDM will be developed for elsewhere 5G (B5G) along with the 6G mobile message scheme. In orthogonal frequency division multiplexing (OFDM), several orthogonal subcarrier indicators through overlapping frequencies remain sent nearby in order to transport data simultaneously. The methods of FAST Fourier transform are the foundation upon which demodulation is built. For the purpose of altering every subcarrier (the signal) at a low symbolic rate, a conventional modulation approach, such as quadrature amplitude variation or else phase change keying, is used. As long as the bandwidth remains the same, this ensures that the overall data rates remain the same as they would be with conventional single-carrier modulation techniques. By altering the number & indexing of active subcarriers that for every OFDM subblock, a new transmission technique has been devised. This approach is capable of transmitting a greater amount of information. There is a difference between OFDM-HNIM, OFDM-SNM & OFDM-IM when compared to itself. This mixed modulation approach is superior to previous OFDM modulation systems in terms of both the spectrum & energy economy that it offers. When it comes to lower modulation rates, such as BPSK, which may deliver great dependability while still being very simple, this is particularly true. It is possible to employ this modulation approach for systems that need improved energy as well as spectrum economy, strong reliability, and minimal complexity among their requirements. In addition to flow and the greatest feasible rate, it pr

关键词： Orthogonal frequency division multiplexing

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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