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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

引用

Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Israt Jahan Md Polash Islam Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Dept. of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN’s promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources.

关键词： Deep learning Training Visualization Accuracy Computational modeling Scalability computer architecture Feature extraction Computational efficiency Sorting

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Duy H. N. Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance.

关键词： Array signal processing Precoding Simulation Quality of service Programming Search problems Prediction algorithms Energy efficiency Real-time systems Optimization

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Three Levels of Agile Planning in a software Vendor Environment 32

Three Levels of Agile Planning in a Software Vendor Environm...

引用

32nd Australasian Conference on Information systems: Information systems for a Sustainable Future, Connectedness, and Social Good, ACIS 2021

作者： Lal, Ramesh Clear, Tony Dept. of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Computer Science and Software Engineering University of Technology Auckland New Zealand

There is a misconception that agile development requires minimal planning effort. In reality, an agile approach for market-driven software development requires highly disciplined, reliable, and accurate planning practices to swiftly plan and develop high-value innovations in a software vendor environment. This study investigated a highly successful international software vendor in Melbourne, Australia, to provide a case study on agile planning practices. Five planning practices were identified which underly successful agile software development for software vendors. These planning practices were driven by agile concepts such as adaptation, self-organizing, cross-functional collaboration, and empowerment/delegation. We constructed a conceptual framework for agile planning practices, the APP (Agile Planning Practices) Framework illustrating the three levels of agile planning. Copyright © 2021 authors.

关键词： software design

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

引用

14th International Conference on Information and Communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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Enabling A Network AI Gym for Autonomous Cyber Agents

Enabling A Network AI Gym for Autonomous Cyber Agents

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Li, Li El Rami, Jean-Pierre S. Taylor, Adrian Rao, James Hailing Kunz, Thomas Defence Research & Development Canada Ottawa Canada Queen's University Dept of Electrical and Computer Engineering Kingston Canada Carleton University Dept. of Systems & Computer Engineering Ottawa Canada

ISBN: (纸本)9798350320282

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL), is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S can train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E, showing full decision proficiency in the emulated 'real' network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real in leveraging RL agents in real-world cyber networks. © 2022 IEEE.

关键词： Reinforcement learning

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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IEEE Region 10 International Conference TENCON

作者： Dhanushka Balasingham Sadeesha Samarathunga Anuththara Bandara Gayantha Godakanda Arachchige Narmada Gamage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure.

关键词： Meters Technological innovation System performance Machine vision Transportation Manuals Real-time systems Rail transportation Reliability Interoperability

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Deepthi Tabitha Bennet Preethi Samantha Bennet Priya Thiagarajan Sundarakantham. K Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better.

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GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

引用

International Conference on Electronics, Information and Communications (ICEIC)

作者： BoJeong Park JongWoo Kim SeoYeong Mun Younglim Choi Hyunseok Kim dept. of Electric Engineering Dong-A University Busan South Korea dept. of Software Innovation Center Dong-A University Busan South Korea dept. of Computer Engineering Dong-A University Busan South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance.

关键词： Solid modeling Three-dimensional displays Annotations Human-robot interaction Transformers Data models Real-time systems Stability analysis Mobile robots Sports

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Federated Learning-Enhanced QoS Multicast Routing to Support RIS and Edge Computing in IoT-Enabled MANETs with CF-mMIMO

Federated Learning-Enhanced QoS Multicast Routing to Support...

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Amalia Amalia Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In this paper, we propose a novel federated learning (FL)-enhanced quality of service (QoS) multicast routing, called FLQMR protocol, in IoT-enabled mobile ad-hoc networks (MANETs) with cell-free massive multiple input multiple output (CF -mMIMO). The main contributions of this paper can be summarized as follows. First, we consider the integration of cross-layer design, reconfigurable intelligent surfaces (RIS), FL, and edge computing to enhance network performance. Second, we design the FL framework to optimize routing decisions by selecting the best paths from the source node to multiple destinations. Third, we employ a cross-layer design that combines the physical layer information (i.e., mobility, position, SE) with the network layer information (i.e., route information) to establish a stable multicast tree from the source node to multiple destinations. The simulation results show that the proposed FLQMR protocol achieves a high packet delivery ratio, low routing delay, and low control overhead.

关键词： Cross layer design Simulation Quality of service Reconfigurable intelligent surfaces Routing Physical layer Ad hoc networks

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Exploiting Secure Multi-Hop Transmissions with NOMA Networks: Performance Analysis

Exploiting Secure Multi-Hop Transmissions with NOMA Networks...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we analyze the secrecy performance of secure multi-hop transmission for the NOMA system. More specifically, the multi-hop transmission can extend the coverage of wireless transmission. However, according to the principle of the wireless medium, an eavesdropper can overhear the legitimate users' transmission, which leads to serious security issues. In order to analyze the relationship between network parameters and secrecy outage probability, we derive exact closed-form expressions for the secrecy outage probability (SOP) of cell center and cell edge users, respectively. The numerical results show that the simulation and analysis results are tightly matched. Additionally, we investigate the impact of the number of hops and the distance on the secrecy performance.

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