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作者： Ansari, Abdul Qadir Rajput, Abdul Qadeer Khan Hashmani, Manzoor Department of Computer Systems and Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (纸本)3540898522

The purpose of this paper is to present a framework design to achieve reverse channel multicast support in IEEE-802.16 networks. The unavailability of reverse channel multicast in IEEE 802.16 networks is one of the issues to be resolved in order to achieve its interoperability with IP networks. The interoperability of IEEE 802.16 networks and IP networks is not yet addressed. Forward channel multicast support is inherent in the IEEE-802.16 standard. Achieving reverse channel multicast support, we can enable IP services requiring native multicast support over IEEE-802.16 networks for seamless interoperation. IP network rudiments are mature enough and not likely to be redefined for any new network design;rather the reverse of this is more likely to be worked on. A framework design to achieve reverse channel multicast support is presented introducing a concentrator layer (Multi Node Access Layer) before Base Station (BS) and MARS (Multicast Address Resolution Server) to handle multicast requests. © 2008 Springer-Verlag.

关键词： Interoperability

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Model- and constraint-based engineering of complex software ecosystems 2

Model- and constraint-based engineering of complex software ...

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2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： Rausch, Andreas Bergner, Sandra Wang, Daning Software Systems Engineering Department of Computer Science TU Clausthal Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9781479952984

No software is an island. It is executed by hardware and interacts with its environment. So-called software systems are complicated hierarchical systems. Competent engineers carefully engineer them. In contrast, complex systems, like biological ecosystems, railway systems and the Internet itself, have never been developed and tested as a whole by a team of engineers. Nevertheless, those complex systems have the ability to evolve without explicit control by anyone, and they are more robust to deal with problems at the level of its constituent elements compared to classical engineered systems. Consequently in this article we introduce the concept of complex software ecosystems comprising of interacting adaptive software systems and human beings. Ecosystems achieve the demanded flexibility and dependability by means of a kind of higher-level regulatory system. Thereby equilibrium is continuously preserved through the appropriate balance between self-adaption and self-control capabilities of ecosystem's participants. We will outline a methodology to support engineering of ecosystems by integrating a model- and constraint-based engineering approach and applying it during design and run time. The open-world semantics of constraints set up a frame for the behavior of participants and the ecosystem itself. Violations of constraints can be identified during design time, but also provide a knowledge transfer to run time. Constraints are additionally monitored and enforced during run time. Thus, we propose an evolutionary engineering approach covering the whole life-cycle for forever active complex software ecosystems. © 2014 IEEE.

关键词： Ecosystems

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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Color space selection for self-organizing map based foreground detection in video sequences

Color space selection for self-organizing map based foregrou...

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2014 International Joint Conference on Neural Networks, IJCNN 2014

作者： Lopez-Rubio, Francisco Javier Lopez-Rubio, Ezequiel Luque-Baena, Rafael Marcos Dominguez, Enrique Palomo, Esteban J. Department of Computer Languages and Computer Science University of Malaga Malaga29071 Spain Department of Computer Systems and Telematics Engineering University of Extremadura Merida06800 Spain

ISBN: (纸本)9781479914845

The selection of the best color space is a fundamental task in detecting foreground objects on scenes. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature;although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and color component weighting selection process is proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos. © 2014 IEEE.

关键词： Self organizing maps

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DEPCAS: an RFID middleware generalization. An example with tracking systems

DEPCAS: an RFID middleware generalization. An example with t...

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2008 International Conference on Wireless Networks, ICWN 2008

作者： Abad, I. Cerrada, C. Cerrada, J.A. Heradio, R. Software Engineering and Computer Systems Department UNED: Distance Learning University of Spain Madrid Spain

ISBN: (纸本)1601320914

Introduction of new standards that impose the use of labels and identification by radio frequency in supply management area has promoted a necessity to research alternatives to solve this question. The different solutions proposed from the software point of view to incorporate the RFID ubiquitous information have been characterized by two extreme positions: of a side the solutions ad-hoc for every specific system, and by another one the solutions based on software packages that give generic support to the established standards. In this article we present our development: DEPCAS (Data EPC Acquisition System) based in the architecture for control systems (SCADAs) applied to the collection of RFID information. An application of DEPCAS is the resolution of heterogeneous situations of items tracking like for example, the luggage tracking, the sport events with massive participation management, the track of goods or merchandising, etc.

关键词： Radio frequency identification (RFID)

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Application of Artificial Intelligence in Rock Tunnel engineering: A Survey on Where and How

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Expert systems 2025年第7期42卷

作者： Xiaojie Yu Ben-Guo He Xu Xu Yicong Zhou Miguel A. Diaz Junxin Chen David Camacho School of Software Dalian University of Technology Dalian China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Department of Computer and Information Science University of Macau Macau China Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in-depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in-depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours.

关键词： artificial intelligence construction methodology rock tunnel engineering tunnel operation

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A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances

arXiv

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arXiv 2025年

作者： Dinitz, Michael Fan, Chenglin Liu, Jingcheng Upadhyay, Jalaj Zou, Zongrui Department of Computer Science Johns Hopkins University United States Department of Computer Science and Engineering Seoul National University Korea Republic of State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University China Management Science & Information Systems Department Rutgers University United States

We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of Õ(h(nW)1/3), where W represents the maximum edge weight;For grid graphs, the same algorithmic scheme achieve additive error of Õ(n1/4√W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. Copyright © 2025, The Authors. All rights reserved.

关键词： Binary trees

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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