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检索条件"任意字段=Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia"

共 603 条记录，以下是21-30 订阅

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Network Intrusion Detection using Dynamic Weighted Aggregation Federated Learning 8

Network Intrusion Detection using Dynamic Weighted Aggregati...

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8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Multimedia University Faculty of Engineering Cyberjaya Malaysia

ISBN: (纸本)9798350315516

Cybersecurity threats, pervasive in our daily activities due to network openness, are exemplified by malicious exploitation of vulnerabilities. these illicit activities, including unauthorized data alterations, emphasize the urgency for effective defense. Integrating Federated Learning (FL) insights from diverse sources presents a compelling approach, addressing network intrusion limitations and heralding a more resilient security paradigm. In this study, we proposed an efficient FL system for network intrusion detection, integrating a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based learning model. the main goal of this research is to enhance intrusion detection while upholding data privacy using FL. We conduct experiments using different numbers of clients (5, 10, and 15) and apply the Dynamic Weighted Aggregation Federated Learning (DWAFL) technique to collaboratively train the model across the clients data. DWAFL is basically an approach where model aggregation incorporates dynamic weightings based on the performance of individual client's model. the experimental results demonstrate that the proposed system achieves 92.2% accuracy with 5 clients, 94.2% with 10 clients and 93.2% with 15 clients, using DWAFL technique. these findings showcase the potential of FL with DWAFL in intrusion detection scenarios, allowing accurate modeling with distributed data sources while preserving data confidentiality. the proposed approach contributes to the development of collaborative learning systems for intrusion detection applications. © 2023 IEEE.

关键词： Intrusion detection

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Utilising the YOLOv3 Algorithm for the Student Posture Recognition System in Classroom Settings 8

Utilising the YOLOv3 Algorithm for the Student Posture Recog...

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8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Nazaruddin, Mohamad Nazmi Zharfan Afirdaus Zainal Abidin, Nor Aminuddin, Raihah Samah, Khyrina Airin Fariza Abu Ibrahim, Asma Zubaida Mohamed Yusoh, Syarifah Diyanah Mangshor, Nur Nabilah Abu Nasir, Siti Diana Nabilah Mohd College of Computing Informatics Mathematics Malacca Malaysia MARA University of Technology College of Computing Informatics Mathematics Malacca Malaysia City University Selangor Malaysia Multimedia University Malacca Malaysia Hull University England United Kingdom

ISBN: (纸本)9798350315516

Effective student-teacher interaction helps transfer knowledge, clarify concepts, and create a conductive learning environment the effectiveness of the interaction can be seen through students' behaviour and various factors, such as pos- ture and gestures in the classroom. However, educators face significant difficulties in tracking each student's performance and behaviour during class therefore, this study focuses on student posture recognition in classroom settings, which is essential for monitoring student behaviour and engagement during lectures the proposed system utilises the YOLOv3 machine learning model for real-time detection. A dataset of student postures was collected from Google Images, and the data was used to train a deep neural network the model was then tested on classroom images and compared to manual annotations the results showed that the model can accurately recognise student postures with high precision, recall, F1-score, and mean average precision (mAP), achieving an average precision of 88%, recall of 89%, F1-score of 88%, and mAP of 95.20% the real-time processing capability of YOLOv3 allows for immediate posture detection during lectures in a classroom;this may help educators monitor student behaviour and engagement. © 2023 IEEE.

关键词： Students

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Challenges in Federated Learning for Resource-Constrained IoT Environments: Energy Efficiency, Privacy, and Statistical Heterogeneity 8

Challenges in Federated Learning for Resource-Constrained Io...

引用

8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Multimedia University Faculty of Engineering Cyberjaya Malaysia

ISBN: (纸本)9798350315516

Due to the extensive Internet availability and interconnections of sensing devices, new sensor-equipped or Internet-of-things (IoT) devices with increased processing and computing capabilities are generating enormous volumes of data. With the use of Machine Learning (ML) techniques, this data may be preprocessed, categorized, and used to forecast upcoming occurrences. However, because data is transferred to and analyzed via a central server, typical ML algorithms have high communication cost, processing latency, privacy concerns, and security problems. To overcome these issues, Federated Learning (FL) employs a decentralized learning strategy in which each client is taught locally using its available data and learning from the global model. Implementation and scalability issues for FL approaches arise from the different computing resource capacities of clients in large-scale networks. In this paper, we discuss open problems and future directions in the FL domain related to devices with limited resources, such as memory, bandwidth, and energy efficient algorithms. We also explore recent real-world applications of FL, highlighting the difficulties of implementing FL algorithms in resource-constrained IoT environments, and explore open issues in the FL domain the main contributions of this study include the challenges and how to deal with them and detailed discussion about energy-efficient algorithms addressing straggler clients, privacy preservation, and handling statistical heterogeneity. For energy-efficient algorithms, alternative neural network architectures such as Spiking Neural Networks (SNNs) is discussed, Mitigating the impact of straggler clients can be achieved by capable clients based on available resources and developing asynchronous training techniques. Privacy concerns can be addressed through privacy-preserving mechanisms like Secure Multiparty Computation (SMC) or Differential Privacy (DP). Handling statistical heterogeneity requires investigating dat

关键词： Energy efficiency

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Design and Performance Analysis of Dielectric Modulated MoTe2Junction-less Tunnel FET based biosensor 8

Design and Performance Analysis of Dielectric Modulated MoTe...

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8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Selvan, Saravana Meng, Lee Kong Zaman, Mukter Solayappan, Maheswaran Nair, Pratap Kumar, Dilip AIMST University Faculty of Engineering and Computer Technology Bedong Malaysia Multimedia University Faculty of Engineering Cyberjaya Malaysia AIMST University Faculty of Applied Sciences Bedong Malaysia

ISBN: (纸本)9798350315516

In this paper, a Molybdenum Telluride (MoTe2) based dielectric modulated Junctionless Tunnel Field Effect Transistor (MoTe2-JLTFET) is proposed and investigated for detecting biomolecules like Biotin, APTES, Cellulose, DNA, and Gelatin. A nanocavity is introduced on the underlap of control gate and polar gate of device for sensing the biomolecules. the variation in the drain current, subthreshold slope (SS) and Ion/Ioff switching ratio are measured to assess the device's sensitivity for biomolecule recognition. MoTe2, a two-dimensional Transition Metal Dichalcogenides (TMDCs) material has been employed in the channel instead of silicon due to its excellent physical and chemical properties. It is observed that the proposed device has less short channel effects, a lower SS and a greater switching ratio than a Silicon-based JLTFET (Si-JLTFET). the impact of biomolecule properties on device performance has been thoroughly analyzed in terms of dielectric constants, doping concentration, threshold voltage and different filling portions in the cavity. © 2023 IEEE.

关键词： Transition metals

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Privacy-Focused Edge AI-on-IoT with Federated Learning 8

Privacy-Focused Edge AI-on-IoT with Federated Learning

引用

8th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2023

作者： Pal, Sauryadeep Tan, Wooi-Haw Foo, Yee-Loo Multimedia University Faculty of Engineering Cyberjaya Malaysia

ISBN: (纸本)9798350315516

Artificial Intelligence (AI) running locally on the Internet-of-things (IoT) Edge devices is referred to as Edge AI. Edge AI can be used to augment IoT applications prioritizing data privacy, low latency, and high availability. Federated Learning (FL) is a Machine Learning technique based on the concept of collaborative model training. With FL, a privacy-focused, flexible Edge AI framework can be created for IoT applications. this paper proposes a FL-based Edge AI, using a Convolutional Neural Network (CNN) for image classification. the primary research objective is to create a flexible, privacy-focused Edge AI framework capable of catering to a variety of IoT Edge devices. To improve accessibility of the Edge AI, optimization has been attempted in terms of finding optimal training parameters for FL and implementing model optimization. Our proposed Edge AI framework is able to accommodate several types of IoT Edge devices, and is capable of being trained via FL. Experimental results showed an accuracy improvement of 19.59% from modifying training parameters, although a balance was needed to prevent overfitting. Implementing model optimization reduced computing resource usage during inference. A CPU time reduction of 59.93% was observed, with only a 3.60% corresponding drop in model accuracy. Furthermore, model optimization also enabled a low-power IoT Edge device to run a complex CNN model, a task it was previously incapable of doing. © 2023 IEEE.

关键词： Internet of things

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Application of Cloud computing and Big Data in Accounting Software 8

Application of Cloud Computing and Big Data in Accounting So...

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8th IEEE international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Jayalakshmi, M. Savaliya, RajeshKumar R Atmakuri, Prashant Rama Prasad, M.V. Kafila Kanika New Horizon College Department: Commerce and Management Marathalli Bangalore560103 India Ambaba Commerce College Mibm & Dica Sabargam B.C.A Department Gujarat Surat394325 India Prasad v Potluri Siddhartha Institute of Technology Department of Cse Andhra Pradesh Vijayawada India Bangalore Campus Nagadenahalli Doddaballapur Taluk Karnataka Bengaluru562 163 India Sr University School of Business Telangana Warangal India Srm Ist Delhi Ncr Campus Modinagar201204 India

ISBN: (纸本)9798350347791

A firm should have the option to stay versatile and responsive with the data it gathers, examinations, and offers in the present impacting world. Along these lines, the cooperation among organizations and data innovation has significantly extended after some time. Cloud computing is one of the most up to date thoughts in data innovation. Numerous gadgets can associate with worldwide servers utilizing this assistance from any area with a web association. Scientists directed a careful study to decide the degree of accounting experts' mindfulness about cloud computing to make sense of this new flanged innovation. Notwithstanding the Web society, advanced innovations that increment business exercises and work with clear independent direction have been influenced by globalization. Big data, cloud computing, and computerized reasoning may essentially improve business the executives and bookkeepers' exhibition. Specifically, the accounting business' fast reception of cloud computing and big data, as well as the jobs that these advances played inside accounting firms, is the focal point of this review. © 2023 IEEE.

关键词： Big data

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Label-Aware Aggregation for Improved Federated Learning 8

Label-Aware Aggregation for Improved Federated Learning

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8th IEEE international conference on Fog and mobile Edge computing (FMEC)

作者： Khalil, Ahmad Wainakh, Aidmar Zimmer, Ephraim Parra-Arnau, Javier Fernandez Anta, Antonio Meuser, Tobias Steinmetz, Ralf Tech Univ Darmstadt Multimedia Commun Lab Darmstadt Germany Tech Univ Darmstadt Telecooperat Lab Darmstadt Germany Univ Politecn Cataluna Network Engn Dept Barcelona Spain IMDEA Networks Inst Leganes Spain

ISBN: (纸本)9798350316971

Federated Averaging (FedAvg) is the most common aggregation method used in Federated learning, which performs a weighted averaging of the updates based on the sizes of the individual datasets of each client. A raising discussion in the research community suggests that FedAvg might not be the optimal method since, for instance, it does not fully take into account the variety of the client data distributions. In this paper, we propose a label-aware aggregation method FedLA, that addresses the biased models issue by considering the variety of labels in the weighted averaging. It combines two main properties of the client data, namely data size and label distribution. through extensive experiments, we demonstrate that FedLA is particularly effective in several heterogeneous data distribution scenarios. Especially when only a small group of the clients is participating in the Federated Learning process. Furthermore, we argue that accurately describing the data distribution is crucial in selecting the appropriate aggregation method. In this regard, we discuss various properties that can be used to describe data distribution and illustrate how these properties can guide the choice of an aggregation method for specific data distributions.

关键词： Federated learning Heterogeneous data distribution non-IID

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Practical Verifiable Computation on Encrypted Data 8

Practical Verifiable Computation on Encrypted Data

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8th IEEE international conference on Big Data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Gong, Xunwu Hu, Bin Xiong, Yunqiang Zhao, Xiaofang Chinese Academy of Sciences Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Beijing Aerospace Automatic Control Institute Beijing China CAS Institute of Intelligent Computing Technology Suzhou China

ISBN: (纸本)9781665480697

We focus on the problem of how to boost greater confidence in computations outsourced to an untrusted cloud server. Naturally, we aim for solutions that cater for the following two security requirements: data confidentiality/privacy and verifiability of computations. this problem is addressed by the research community through an intriguing paradigm combining a verifiable computation (VC) and a fully homomorphic encryption (FHE). Despite continuing advances in achieving efficient VC and FHE, existing approaches that meet both requirements simultaneously are expensive and impractical for servers and clients. In this work, we first introduce an interesting primitive, called versatile message authenticators, to achieve a verifiable outsourced computation on encrypted data and then give a concrete construction. Without the involvement of FHE, our scheme mainly depends on a pseudo-random function and a adapted Homomorphic MAC scheme. Our solution is proven secure in a strong security model where the adversary is allowed to issue any verification queries. Furthermore, it allows for arbitrary composition and generates constant-size tags. However, our construction just supports linear functions. Finally, our security proof shows its semantic security and unforgeability. © 2022 IEEE.

关键词： Cloud computing

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EgpuIP: An Embedded GPU Accelerated Library for Image Processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. the image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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Analyzing and Predicting the Sales Forecasting using Modified Random Forest and Decision Tree Algorithm 8

Analyzing and Predicting the Sales Forecasting using Modifie...

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8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Ajay, Ryali Sabin Joel, R. Om Prakash, P.G. Srm Institute of Science and Technology Department of Computational Intelligence Chennai India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattangaluthur India

ISBN: (纸本)9798350396638

Every element of life is evolving as a consequence of machine learning, which has also had a substantial effect on real-world circumstances. there are transformational uses for machine learning in every area, including education, healthcare, engineering, commerce, entertainment, and transportation, to mention a few. the normal method of establishing sales and marketing objectives without considering the purchasing patterns of customers does not assist organization's in keeping up with the speed of the competitive market. Significant developments have occurred in the industry of sales and marketing due to advances in machine learning. Due to these advancements, it is now feasible to predict a variety of crucial aspects, including the demographics of the target market and sales estimates for the next years. this makes it easy for the sales team to develop company-expansion plans. this goal of the proposed system is to provide a formula for estimating Big Mart Companies' future sales based on their past sales. Machine learning models including XG BOOST, Decision Tree Regression, Lasso Regression, Ridge Regression, and Linear Regression. the prediction takes into account data elements like age group, gender, job status, product category, etc. © 2023 IEEE.

关键词： Sales

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