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Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

computer Systems science & Engineering 2023年第2期44卷 1295-1307页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

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computer Systems science & Engineering 2023年第2期44卷 991-1007页

Establishing a system for measuring plant health and bacterial infection is critical in ***,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been *** inspection can determine which plants reﬂect the quantity of green light and near-infrared using infrared light,both visible and eye using a *** goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classiﬁcation *** neural networks in unmanned aerial vehi-cles image were *** convey this interest,the rice’s health and bacterial infec-tion inside the photo were *** project entailed using pictures to identify bacterial illnesses in *** shape and distinct characteristics of each infection were *** symptoms were deﬁned using machine learning and image processing *** steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by *** proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

关键词： Bacterial infection detection adaptive deep learning unmanned aerial vehicles image retrieval

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An Analysis of Image Splicing Detection based on Sensor Noise Levels using Principal Component Analysis (PCA) Estimation against ISO Variation Settings 1

An Analysis of Image Splicing Detection based on Sensor Nois...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Mohamad Rodzi, Nur Syazwani Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Tun Hussein Onn Malaysia Centre for Information Security Research Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Faculty of Computing Pahang Malaysia

ISBN: (纸本)9798350387728

Image splicing is a process where parts from two or more images are combined to create a single composite image. Since different devices may use varying ISO settings, detecting image splicing could involve analysing the combination of elements from multiple images. This research investigates the effectiveness of using sensor noise levels to detect image splicing, with a particular focus on the influence of different ISO settings. In this research, a comprehensive methodology is used to examine how different ISO settings affect splice detection performance. An image splice detection method is implemented to investigate robustness to different ISO settings in two datasets. The method calculates an estimate of sensor noise based on Principal component Analysis (PCA). The values are then weighted to locate the spliced area. Two datasets are collected, and each dataset shows the combination of two ISO settings images. Experimentation and analysis have revealed a clear difference in detection performance between ISO 800 with ISO 200 and ISO 400 with ISO 320 settings. The results show that the average F-measure score for ISO 400 spliced to ISO 320 is 93.64% which is higher than ISO 800 spliced to ISO 200, which is 87.92%. These mean that lower ISO settings result produce more accurate splicing detection, likely due to lower noise levels in the captured images. As the scores for ISO 400 with ISO 320 are more stable and higher, this stability implies that the method performs consistently well under lower ISO settings, making it more reliable for practical applications. © 2024 IEEE.

关键词： digital forensics Image splicing detection ISO settings sensor noise splicing detection techniques

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T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Extractive Long Document Summarization 6

T5-LDSum: Leveraging T5 Transformer in Hybrid Abstractive-Ex...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Magdy, Mostafa Abdelbaky, Abdelrahman M. Mohamed, Ensaf Hussein Helwan University Master of Computer Science Faculty of Computers and Artificial Intelligence Egypt Helwan University Bachelor of Computer Science Faculty of Computers and Artificial Intelligence Egypt Nile University Computer Science School of Information Technology and Computer Science Giza Egypt Helwan University Faculty of Computers and Artificial Intelligence Computer Science Department Egypt

ISBN: (纸本)9798350378511

In the era of digital information overload, the ability to summarize books efficiently emerges as an invaluable skill. Book summarization condenses extensive texts into digestible, concise summaries, enabling readers to grasp the essence of a book without committing to reading it in its entirety. In scholarly research, various techniques for text summarization are employed, including extractive summarization, abstractive summarization, and hybrid approaches. This paper presents a novel hybrid method that leverages the strengths of both extractive and abstractive techniques, with a particular focus on the T5 text-to-text transformer model. Our methodology begins with a BERT-based extractive model paired with the LexRank algorithm to generate preliminary summaries. These summaries are subsequently refined using the T5 transformer model, known for its powerful text generation capabilities. We conducted two empirical studies using Oscar Wilde's 'The Picture of Dorian Gray,' specifically focusing on Chapter 13. Our experiments analyzed the efficacy of summaries derived from the top 20% and top 60% of sentences ranked by the extractive model. The findings indicate a consistent pattern: summaries based on the 60% sentence extraction significantly outperform those from the 20% extraction across all assessed metrics-Rouge, precision, recall, F1 scores, and human evaluations. The integration of the T5 transformer model in the refinement process is highlighted as a key component in achieving high-quality, coherent summaries. © 2024 IEEE.

关键词： abstractive summarization Book summarization evaluation metrics extractive summarization hybrid summarization

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Identification and Classification of Crowd Activities

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computers, Materials & Continua 2022年第7期72卷 815-832页

作者： Manar Elshahawy Ahmed O.Aseeri Shaker El-Sappagh Hassan Soliman Mohammed Elmogy Mervat Abu-Elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

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Feature Selection in Associative Classification-A Review and Comparative Analysis 24

Feature Selection in Associative Classification-A Review and...

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24th International Arab Conference on information technology, ACIT 2023

作者： Alazaidah, Raed Al-Qerem, Ahmad Qasem, Mais Haj Al-Shaikh, Ala'a Almilli, Nabeel Injadat, MohammadNoor Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798350384307

Feature selection is one of the most important and significant steps that highly affects the final accuracy of any classification model. It aims to determine the most significant features that help in accurately predicting the class label for any new case. This research aims to survey and identify the best feature selection method that suits algorithms belong to Associative Classification (AC). Hence, nine different feature selection methods have been evaluated and compared using five AC based algorithms with respect to Accuracy metric. Results revealed that ABB-IEP and ABB-LIU methods show the best performance with datasets that contain nominal features only, while Relief feature selection method showed the best Accuracy with datasets that comprise Real and Integer features. © 2023 IEEE.

关键词： associative classification classification data mining data science feature selection pre-processing

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6G on the Horizon:Technologies, Requirements, Trends, and Potential Techniques 4

6G on the Horizon:Technologies, Requirements, Trends, and Po...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yunus, Nur Arzilawati Md Hanapi, Zurina Mohd Kamarudin, Shafinah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Communication Technology and Network Selangor 43400 Malaysia

ISBN: (纸本)9798350395914

The advance of sixth generation (6G) wireless communication technology represents a paradigm shift in the field of telecommunications, promising unprecedented levels of connectivity, speed, and reliability. This review delves into the fundamental requirements driving the development of 6G, highlighting the need for ultra-reliable and low latency. By examining cutting-edge technologies such as terahertz communication, massive Multiple-Input Multiple-Output (MIMO), and Artificial Intelligence (AI) driven network optimization, this paper identifies key trends shaping the future of wireless communication networks. Moreover, it explores potential innovations including holographic communication, self-organizing networks, and quantum communication that have the potential to redefine the capabilities of 6G networks and unlock new opportunities for connectivity and collaboration. © 2024 IEEE.

关键词： Quantum communication

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Data Driven Vibration Control:A Review

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1898-1917页

作者： Weiyi Yang Shuai Li Xin Luo Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Faculty of Information Technology and Electrical Engineering University of OuluOulu 90570 VTT-Technology Research Center of Finland Oulu 90570Finlan College of Computer and Information Science Southwest UniversityChongqing 400715China

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic *** shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC *** convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration *** on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of *** doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

关键词： Data driven vibration control(DDVC) data science designing method feedforward control industrial robot input shaping optimizing method residual vibration

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IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

IMAN: An Adaptive Network for Robust NPC Mortality Predictio...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huo, Yejing Huang, Guoheng Cheng, Lianglun He, Jianbin Chen, Xuhang Yuan, Xiaochen Zhong, Guo Pun, Chi-Man Guangdong University of Technology School of Computer Science and Technology China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Macao Polytechnic University Faculty of Applied Sciences China Guangdong University of Foreign Studies School of Information Science and Technology China

ISBN: (纸本)9798350386226

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom

关键词： Patient treatment

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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