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arXiv 2024年

作者： Khanna, Sandeep Chattopadhyay, Chiranjoy Kundu, Suman Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India School of Computing and Data Sciences FLAME University India

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

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A Loop Optimization Method for Dataflow Architecture 24

A Loop Optimization Method for Dataflow Architecture

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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： Fan, Zhihua Li, Wenming Liu, Tianyu Tang, Shengzhong Wang, Zhen An, Xuejun Ye, Xiaochun Fan, Dongrui State Key Lab of Processors Institute of Computing Technology Cas Beijing100049 China School of Computer and Control Engineering Ucas Beijing Beijing100190 China

ISBN: (纸本)9798350319934

Dataflow architecture is a promising parallel computing platform that provides high performance, high efficiency and flexibility. Since the execution of a loop is usually the most time-consuming in a program, extracting sufficient loop-level parallelism is an essential task of dataflow architecture. However, existing methods often result in low execution performance and poor hardware utilization due to the imbalance among dataflow graph nodes and the non-preemptive mechanism. To tackle these problems, this paper makes three contributions: 1) A highly effective dataflow graph balancing method is developed to improve the utilization. 2) An enhanced dataflow execution model is proposed, which adapts a novel preemptive mechanism to further improve the hardware utilization and performance. 3) A decoupled dataflow architecture is designed to efficiently support our proposed dataflow execution model. Experiment shows that our methods can achieve an average of 2.61 ×performance improvement and 2.5 ×utilization improvement compared to the state-of-the-art methods with acceptable overhead. © 2022 IEEE.

关键词： Computer architecture

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Dual-Band, Polarization Insensitive, and Flexible EM Wave Absorber with High Angular Stability and Low Cross Reflection Level 3

Dual-Band, Polarization Insensitive, and Flexible EM Wave Ab...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Kumar, Awanish Reddy, G. Shrikanth Narayan, Shiv School of Computing and Electrical Engineering Indian Institute of Technology Mandi. Mandi India Center of Electromagnetic Division CSIR-National Aeronautical Laboratory Bangalore India

ISBN: (纸本)9789463968058

This paper presents a dual-band polarization-insensitive, highly angular stable and flexible EM wave absorber with low cross-polarization reflection. The proposed unit cell has two resonators C and D which offers absorptions at two distinct frequencies at 8.7 and 11.5 GHz frequency in the X band. This FSS unit cell structure has four-fold symmetry make it polarization-insensitive. The absorbance mechanism of the proposed structure is explained with the help of electric and magnetic coupling along with effective impedance plot. The simulation results reveal that for both bands, the proposed absorber is polarization insensitive and angular stable up to 600. Along with this study, reflection due to cross-polarization is also analyzed and included in the absorbance equation, confirming that the proposed design is an absorber, not a polarization converter. Finally, the absorber prototype is fabricated and tested under different conditions, and the measurement results perfectly match the simulation result, which verified that it is working. © 2022 URSI.

关键词： Absorbance

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Research on Fault Diagnosis Method Based on Structural Causal Model in Tennessee Eastman Process 7th

Research on Fault Diagnosis Method Based on Structural Causa...

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7th International Conference on computing, Control and Industrial engineering, CCIE 2023

作者： Pu, Haoyuan Liu, Jie Chen, Zhi Yang, Xiaohua Ren, Changan Xu, Zhuoran Jian, Yifan School of Computer Science University of South China Hengyang China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

ISBN: (纸本)9789819927296

In the practical application of fault diagnosis in large-scale chemical systems, due to the danger of faults, the machine learning diagnosis methods used in current research often face the problem of scarcity of fault samples. This makes it difficult for the model to be trained effectively, thus affecting the fault detection rate of the model. Therefore, for the fault diagnosis of chemical system with small sample data, this paper proposes a diagnostic method that uses structural causal model to further combine the diagnostic results of the two machine learning methods, thereby improving global diagnostic performance. With the help of powerful system description ability of structural causal model, we can establish a causal graph for diagnostic process of the chemical system, and accurately construct the corresponding structural equations for the various diagnostic results of the two machine learning methods to combine them, thus improve the fault detection rate. The global results make up for the shortcomings of single machine learning method in small sample chemical system fault diagnosis. The verification on the chemical system simulation platform Tennessee Eastman process shows that, compared with the diagnosis results of the selected Gaussian Naive Bayes method and the K-nearest neighbour method, the results obtained by our method are effectively improved in fault detection rate of each fault in TEP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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Performance Evaluation of QoS in MAODV Routing Protocol in MANETS

Performance Evaluation of QoS in MAODV Routing Protocol in M...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Dekka, Satish Sambana, Bosubabu Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Reddy, Kuppireddy Krishna Vizianagaram JNTU-GV Andhra Pradesh Vizianagaram India School of Computing Mohan Babu University Andhra Pradesh Tirupathi India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Electrical and Electronics Engineering Mother Theresa Institute of Engineering and Technology Palamaner JNTUA Andhra Pradesh Anantapur India

ISBN: (纸本)9783031513374

A Mobile Ad-hoc Network (MANET) is a wirelessly connected network of mobile devices that configures itself without the need for infrastructure. With the freedom to travel in any direction, every device within a MANET will regularly switch links with other devices. Each device operates as a router. The key difficulty in developing a MANET is ensuring that every device has the means to constantly update the data needed for accurate traffic routing. These networks can function alone or they can be linked to the wider Internet. Since MAODV is the reactive protocol linked to the Ad hoc On-Demand Distance Vector (AODV) routing protocol, it is comparable to AODV in terms of packet formats. The unicast Route Table and the packet types for the Route Request and Route Reply are derived from those used by AODV. In a similar vein, AODV defines a large number of the configuration settings that MAODV uses. A mobile node that wants to join or participate in a group inside an ad hoc network can do so dynamically, autonomously, and across several hops using the Modified Ad hoc On-Demand Distance Vector (MAODV) protocol. For managing routing in a mobile setting, there are various protocols. The MAODV (Modified Ad hoc On-Demand Distance Vector) routing protocol used in MANETs is therefore presented here. Packet delivery ratio, average end-to-end delay, and throughput are the measures utilized in simulation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile ad hoc networks

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning 21

Analysis and Treatment of Missing Data in Happiness Score Us...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Touati, Wassim El Touati, Yamen Hadj-Alouane, Nejib Ben Jemai, Abderrazek Polytechnic School of Tunisia Carthage University SERCOM Laboratory Tunis Tunisia Northern Border University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia ENIT University of Tunis EI Manar OASIS Laboratory Tunisia American University in Dubai Electrical Engineering and Computer Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9798350378078

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible. © 2024 IEEE.

关键词： Adversarial machine learning

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Deng, Zehang Sun, Ruoxi Xue, Minhui Wen, Sheng Camtepe, Seyit Nepal, Surya Xiang, Yang Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC Australia CSIRO's Data61 Cybersecurity and Quantum Systems Group Australia

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6× to over 100× more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead. © 2004-2012 IEEE.

关键词： Federated learning

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Projective Exploration on Individual Stress Levels using Machine Learning 2

Projective Exploration on Individual Stress Levels using Mac...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Nagendra Prabhu, S. Singh, Amarjeet Bhavya, B. Sreeja, K. Bhargavi, P. Srm Institute of Science Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665400527

Recently, Stress Prediction in every individual based on their profile and behaviour is a challenging task in the current sector. Current system is a manual process where it is difficult to identify the stress in the college students or employees. There is no automation for any stress prediction. System uses machine learning algorithms or AI algorithms to find out the stress levels, these technologies are used for application development. The purpose of this project is to reduce stress in students and employees. Computer science has come a long way in the last few years. It is massive and multifaceted. It has been used in a variety of applications to meet the basic needs of human society. In the field of healthcare, machine learning has made significant progress. Stress is a deadly disease that kills a large number of people worldwide. How machine learning techniques can assist in reducing the risk of stress prediction, is examined in this study. © 2022 IEEE.

关键词： Personnel

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Design and Implementation of Web Crawler System Based on Python 8

Design and Implementation of Web Crawler System Based on Pyt...

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8th International Conference on Information Systems engineering, ICISE 2023

作者： Pan, Aimin Pan, Huafeng Wuhan Donghu University School of Electronics and Information Engineering WuHan430212 China National University College of Computing and Information Technologies ManilaPH 1008 Philippines Shanghai JHeat Technology Co. LTD 201800 China

ISBN: (纸本)9798350307009

Web crawlers are programs to download resources from internet, one of basic componments of search engines. Using web crawler can collect data in batch es, which greatly improves the speed an d efficiency of data analysis. Python language is simple and efficient, known as "glue language", with rich third-party library, which is very convenient to handle web pages and links. This paper introduces the types of web crawler, working principle and Python crawler technology, and constructs a crawler system for crawling web images. © 2023 IEEE.

关键词： Web crawler

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