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A Novel Metadata Based Multi-Label Document Classification Technique

computer systems Science & Engineering 2023年第8期46卷 2195-2214页

作者： Naseer Ahmed Sajid Munir Ahmad Atta-ur Rahman Gohar Zaman Mohammed Salih Ahmed Nehad Ibrahim Mohammed Imran BAhmed Gomathi Krishnasamy Reem Alzaher Mariam Alkharraa Dania AlKhulaifi Maryam AlQahtani Asiya A.Salam Linah Saraireh Mohammed Gollapalli Rashad Ahmed Barani Institute of Information Technology(BIIT) PMAS Arid Agriculture UniversityRawalpindi46000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Faculty Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia College of Business Administration Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is *** a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on *** engines,indexing services,and digital libraries have been searching for such publications over the ***,getting the most relevant articles against the user requests is yet a *** is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject *** overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the *** proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(***)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

关键词： Multilabel classification indexing metadata content/data mining

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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A Hierarchical Traffic Sign Image Recognition Algorithm Keeping the Local Topology 7

A Hierarchical Traffic Sign Image Recognition Algorithm Keep...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Liu, Longhui Gong, Wenhui Wang, Wenjie Zeng, Zhiqiang Li, Wei Xiamen Global Navigation Satellite System Development & Application Co. Ltd. Xiamen China School Of Computer And Information Engineering Xiamen University Of Technology Xiamen China

ISBN: (纸本)9798350361445

Traffic sign recognition is crucial in enabling intelligent driving. This paper proposes a robust hierarchical traffic sign image recognition algorithm that maintains local topology. The algorithm is divided into two matching levels: rough matching and fine matching. The multi-scale image's Oriented Fast and Rotated BRIEF (ORB) [1] are extracted in coarse matching. Hamming distance is adopted to measure the similarity of feature points to execute rough and fast matching. In precise matching, the matched candidate feature points are further filtered using the number of features of surrounding points, that is, the Shape Context [2] feature, to achieve precise matching. This hierarchical matching method maintains the local topological characteristics of the image while performing robust and fast matching. In the experiment, the data set provided by Changsha University of Science and technology was used to assess the performance of the algorithm presented in this article. © 2024 IEEE.

关键词： Traffic signs

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning system

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computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning system(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： Natural language processing information retrieval image captioning deep learning metaheuristics

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Binary and Multi-Class Classification on the IoT-23 Dataset

Binary and Multi-Class Classification on the IoT-23 Dataset

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2023 Advances in Science and Engineering technology International Conferences, ASET 2023

作者： Ahli, Alia Raza, Ayesha Akpinar, Kevser Ovaz Akpinar, Mustafa Rochester Institute of Technology Computing Security Department Dubai United Arab Emirates Higher Colleges of Technology Faculty of Computer Information System Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

In the recent years, Internet of Things (IoT) technology has become widespread and is commonly used by organizations for many different purposes. With the increase in the demand for this technology, new security challenges arise. It becomes difficult for cybersecurity experts to administer security measures and prevent the IoT network from malicious attacks. This paper proposes the use of machine learning algorithms - Random Forest (RF), Multi- layer Perceptron (MLP) and Gradient Boosting (GB) to detect malicious traffic flows and anomalies in the IoT network. For this purpose, Aposemat IoT-23 dataset is used, and the models are built, trained and tested on it. Two types of classification are considered - binary and multi-label. Random Forest and Gradient Boosting classifier were able to achieve high accuracies of 98.6% and 97.7% respectively. © 2023 IEEE.

关键词： Internet of things

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Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

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Soft Computing 2024年第20期28卷 11987-12003页

作者： Boccia, Maurizio Mancuso, Andrea Masone, Adriano Sterle, Claudio Department of Electrical Engineering and Information Technology University of Naples "Federico II" Via Claudio 21 Naples80125 Italy Institute for System Analysis and Computer Science "Antonio Ruberti" National Research Council of Italy via dei Taurini 19 Rome00185 Italy

This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure. © The Author(s) 2024.

关键词： Operating rooms

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High-performance self-powered ultraviolet to near-infrared photodetector based on WS_(2)/InSe van der Waals heterostructure

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Nano Research 2023年第5期16卷 7851-7857页

作者： Jinping Chen Zhen Zhang Yi Ma Jiying Feng Xiaoyu Xie Xiaoxuan Wang Aoqun Jian Yuanzheng Li Zhuxin Li Heng Guo Yizhi Zhu Qiannan Cui Zengliang Shi Chunxiang Xu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education&College of Information Engineering and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China

van der Waals heterostructures(vdWHs)based on two-dimensional(2D)materials without the crystal lattice matching constraint have great potential for high-performance optoelectronic ***,a WS_(2)/InSe vdWH photodiode is proposed and fabricated by precisely stacking InSe and WS_(2)flakes through an all-dry transfer *** WS_(2)/InSe vdWH forms an n–n heterojunction with strong built-in electric field due to their intrinsic n-type semiconductor characteristics and energy-band alignments with a large Fermi level offset between WS_(2)and *** a result,the device displays excellent photovoltaic behavior with a large open voltage of 0.47 V and a short-circuit current of 11.7 nA under 520 nm light ***,a fast rising/decay time of 63/76μs,a large light on/off ratio of 105,a responsivity of 61 mA/W,a high detectivity of 2.5×10^(11) Jones,and a broadband photoresponse ranging from ultraviolet to near-infrared(325–980 nm)are achieved at zero *** study provides a strategy for developing high-performance self-powered broadband photodetectors based on 2D materials.

关键词： two-dimensional materials van der Waals heterostructure self-powered photodetector photocurrent

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A lightweight point cloud analysis network based on graph walking 8

A lightweight point cloud analysis network based on graph wa...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Wenju Ji, Qianqian Sha, Liye Cui, Liu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

Based on the irregularity and permutation invariance of point clouds, graph neural networks have been widely used in 3D point cloud analysis tasks in recent years. However, these works suffer from insufficient feature extraction at long distances and high computational resource overhead. To this end, this paper proposes a graph walk based lightweight network for 3D point cloud analysis tasks. Specifically, after constructing the nearest neighbor graph in the downsampled point cloud, the MLP module in the original network was replaced by multiple nonparametric components to reduce the number of parameters of the model. Then, the graph walking mechanism was used to extract the long-distance features and geometric structure features of the point cloud. Finally, before point classification and segmentation, the residual pooling module is used to fuse the features to obtain the global features. Compared with the benchmark network DGCNN, the classification accuracy on the ScanObjectNN dataset is improved by 4.2%, the segmentation accuracy on the ShapeNetPart dataset is improved by 1.1%, and the number of model parameters is reduced by 33.3%. Experiments show that the design method has obvious effect on 3D point cloud analysis tasks. © 2023 IEEE.

关键词： Graph neural networks

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Integration of Li-Fi and IoT for Sixth-Generation (6G) Communication systems

Integration of Li-Fi and IoT for Sixth-Generation (6G) Commu...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Ajay Sharma Peter Albert Xuereb Lalit Garg Computer Information System Faculty of Information and Communication Technology University of Malta Msida MALTA

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Light Fidelity (LiFi) to enhance future communication solutions along with IoT connectivity is a must to design the Sixth Generation (6G) networks. LiFi, which uses visible light for data communication, offers several advantages over RF-based technologies, including a high signaling rate, better security, and reduced interference. This paper aims at analyzing LiFi's possibilities of integrating with IoT to solve the problems of poor coverage and sensitivity to ambient light and interconnections with existing RF networks. The proposed network structure to integrate both LiFi and RF does provide a path for smooth transition between LiFi and RF to maintain the connection and performance. New modulation techniques and enhanced security schemes are incorporated to overcome technical constraints and gain better communication with security. In projecting how LiFi can enhance green technology, the study shows that through using energy-efficient LED, LiFi is capable of complementing green technology for energy-efficient communication of IoT via 6G networks.

关键词： 6G mobile communication Radio frequency Electric potential Protocols Green products Modulation Interference Light fidelity Energy efficiency Security

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