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Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Deep Learning Application in PQRS Segment Detection in ECG Data

Deep Learning Application in PQRS Segment Detection in ECG D...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pandey, Amit Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350378092

The electrocardiogram is a vital piece of information about a person's health that provides measurements of changes in the cardiovascular system. When using complex software modules to diagnose diseases, the clinical ECG signal is always tainted by power line interference and electromagnetic fields, leading to inaccurate ***, it's critical to reduce these ECG data acquisition recording inaccuracies in order to perform precise clinical analysis. We are working on an algorithm that will split the data array of three successive ECG signals into three equivalents. The Empirical Mode After decomposing the data matrix and running it through the Independent Component Generation algorithm, we will identify and remove any noisy components using deep learning scheme. To obtain an error-free result of ECG data, we will then apply reverse ICA and EMD features for deep learning. © 2024 IEEE.

关键词： Deep learning

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An energy-efficient task and virtual machine placement in virtualised cloud server using FY-SFLA and RMMS-DLVQ

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International Journal of Networking and Virtual Organisations 2023年第2期29卷 144-167页

作者： Sudhakar, E.P. Saravanan, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Creating infrastructures, virtual servers, computing resources, along with devices is termed virtualisation. In this methodology, to augment resource usage along with to mitigate the total power consumption, mapping of a group of virtual machine (VM) onto a set of physical machines (PM) is performed in a data centre (DC). Nevertheless, a crucial challenge is presented by the VM allocation together with the higher energy consumption (EC) of cloud data centres (CDC). Thus, to alleviate the resource wastage along with to mitigate the DCs' EC, an effectual Fisher Yates-Shuffled frog leaping algorithm (FY-SFLA) is proposed here: 1) task feature extraction;2) resource information extraction;3) task separation by utilising cosine distance - K means algorithm (CD-KMA);4) task placement in VM by employing the FY-SFLA task;5) VM status identification by deploying random mutation monkey search deep learning vector quantisation (RMMS - DLVQ) are '5' phases comprised in the proposed methodology. © 2023 Inderscience Enterprises Ltd.

关键词： Virtual machine

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HFBLGERC: Design of an Efficient Hybrid Fusion of BiLSTM, BiGRU, with ES-RNN for Responsive Resource Scheduling 15

HFBLGERC: Design of an Efficient Hybrid Fusion of BiLSTM, Bi...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kazi, Aihtesham Chaudhari, Dinesh Bajaj Institute of Technology Department of Computer Engineering Wardha India Jawaharlal Darda Institute of Engineering & Technology Computer Science & Engineering Yavatmal India

ISBN: (纸本)9798331300579

The efficient scheduling of tasks on virtual machines (VMs) is paramount in cloud computing environments. The complexity and dynamism of today's applications require a more insightful and adaptive approach to task allocation to ensure optimal resource utilization and service delivery. Traditional scheduling approaches often fall short when it comes to considering the multi-dimensional attributes of tasks and VMs, such as makespan, deadline, memory, and bandwidth requirements. These methodologies lack the ability to dynamically adapt to the ever-evolving requirements of tasks and the capacities of VMs, leading to suboptimal performance and resource wastage. In this paper, we present a novel approach that fuses BiLSTM & BiGRU with Exponential Smoothing Recurrent Neural Network (ES-RNN) to create a more robust and adaptive task scheduling mechanism under real-time scenarios. This model holistically assesses task capacity based on its makespan, deadline, memory, and bandwidth requirements. Similarly, VM capacity is evaluated based on its RAM, MIPS, bandwidth, and the number of processing elements. The fusion of these advanced neural architectures provides a deeper understanding of the task-VM mapping, enabling a more intelligent and efficient scheduling decision. Our approach demonstrates a marked improvement over traditional techniques, with tangible benefits such as reduced makespan by 4.9% and improved VM computation efficiency by 3.5%. The practical implications of our methodology are profound. By integrating our model into real-world cloud environments, organizations can expect to see an enhanced deadline hit ratio by 1.5%, ensuring that critical tasks meet their time-sensitive objectives. Moreover, the decision-making process becomes significantly more agile, resulting in a decision delay reduction of 4.5%, thereby promoting more responsive and efficient cloud computing operations. This work paves the way for a new era of intelligent cloud resource management, opt

关键词： Decision making

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The Future of Intelligence: Advancements and Challenges in AI and Machine Learning 15

The Future of Intelligence: Advancements and Challenges in A...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pushpa, R. Rekha, H. Akshaya Institute of Technology Department of computer Science and Engineering Tumakuru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumakuru India

ISBN: (纸本)9798331300579

The incorporation of Machine Learning (ML) and Artificial Intelligence (AI) technologies across diverse industries presents considerable potential for both economic expansion and societal ***, which includes both Narrow and General forms, in conjunction with ML, enables data-driven decision making and automation in numerous industries, including healthcare, finance, and ***, the extensive implementation of these technologies is beset by obstacles such as ambiguity in algorithms, ethical concerns, and *** paper studies the developments and obstacles in Al and ML, as well as their practical implementations in nanotechnology, medicine, governance, and *** the potential for economic benefits and societal advancements is apparent, it is imperative to prioritize responsible and equitable implementation of Al by addressing organizational shortcomings, personnel issues, and economic *** is imperative that governments, organizations, and academia work together in concert to surmount these challenges and fully exploit the capabilities of Al and ML for the betterment of *** paper emphasizes the significance of conducting a thorough analysis of the ethical, societal, and economic consequences in order to effectively achieve the potential of Al and ML. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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NADR-Net: A Deep Learning Framework for Predicting Neurological Adverse Drug Reactions Using 17 Molecular Descriptors

NADR-Net: A Deep Learning Framework for Predicting Neurologi...

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2024 International Symposium on Computing and Intelligent Systems, SCI 2024

作者： Chaurasia, Anushka Kumar, Deepak Yogita Department of Computer Science and Engineering National Institute of Technology Meghalaya India Department of Computer and Engineering National Institute of Technology Kurukshetra India

Neurological adverse drug reactions (NADRs) pose a significant clinical challenge as they can have a profound impact on patient health and treatment outcomes. While diverse drug descriptors have been employed for neurological ADR prediction, the potential of using 17 molecular descriptors for this purpose has not been explored. To address this, a multilabel NADR-Net and MLSMOTE-based framework have been proposed for neurological adverse drug reaction prediction. The data for 17 MD and ADRs were extracted from PubChem and ADRECs databases and then mapped based on drug ID. The resulting dataset contained information on 2160 drugs, including their molecular properties, and 1030 ADRs. The methodology was then applied to this dataset, and it showed promising results in terms of hamming loss, precision, true positive rate, f1 score, and ROC-AUC. This study highlights the potential of using molecular descriptors for predicting neurological ADRs, which could improve patient outcomes and drug safety. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep neural networks

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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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AgriAI: Revolutionizing Agriculture with Data science 2

AgriAI: Revolutionizing Agriculture with Data Science

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Raut, Nitin B. Ashwin, U. Aswin, S. Bala Selva Vishnu, N. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350365269

In this ambitious agricultural project, cutting-edge technologies such as Machine Learning (ML) and Deep Learning (DL) are harnessed to revolutionize farming practices in India. The primary focus lies in automating vital tasks like crop disease detection, crop recommendation, and fertilizer suggestion, which are traditionally laborious and time-consuming for farmers. Leveraging recent advancements in ML algorithms, a comprehensive solution is envisaged. This innovative approach involves the integration of sophisticated models into a user-friendly website, creating a seamless user experience. Through this platform, farmers gain access to automated tools that significantly enhance their crop management strategies. The platform provides a comprehensive solution by combining deep learning-based crop disease detection mechanisms, intelligent crop recommendation systems, and effective fertilizer recommendations derived from machine learning algorithms. This combination of technologies helps farmers maximize their overall crop yields and minimize losses caused by diseases in addition to streamlining farming procedures. The synergy of ML, and DL on a unified web interface stands as a testament to the transformative power of technology in agriculture, marking a pivotal step toward sustainable and efficient farming practices in India. © 2024 IEEE.

关键词： Support vector machines

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2024年第16期84卷 1-29页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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