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International Conference on science Technology engineering and Management (ICONSTEM)

作者： B. Radha Jaya Bharathi R R. Vidhya Muthulakshmi K. Vidhya R. Vijaya Kumar Reddy Ruchika Rondon Department of ICT and Cognitive Systems Sri Krishna Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering KPR Institute of Engineering and technology Arasur Tamil Nadu India Department of Computer Science Engineering Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Petroleum Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems.

关键词： Cloud computing Data privacy Accuracy Refining Mathematical models Cryptography STEM

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Alleviate sidewall damage of InGaN green micro-LEDs by atomic layer etching

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Optics letters 2025年第11期50卷 3756-3759页

作者： Zhiyuan Liu Haicheng Cao Tingang Liu Yi Lu Xiao Tang Zixian Jiang Na Xiao Xiaohang Li Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

In this work, atomic layer etching (ALE) technology is demonstrated to alleviate the sidewall damage generated during the mesa etching process of InGaN micro-LEDs. TEM images verify the existence of the sidewall damage and its mitigation after 200-cycle ALE sidewall treatment. The defect-related leakage current density significantly decreases from 3 × 10 to 7 × 10 A/cm at -20 V bias through sidewall treatment. InGaN green micro-LEDs (11 µm) with ALE sidewall treatment show a more than 10% enhancement in external quantum efficiency compared to untreated reference devices. This work provides a new, to our knowledge, perspective on addressing the sidewall effect in micro-LEDs, aiding the realization of high-efficiency InGaN micro-LEDs in the near term.

关键词： Atomic force microscopy Indium gallium nitride Indium tin oxide Light extraction Quantum efficiency Surface layers

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EEG Microstate Analysis for Diagnosis of Children with ASD

EEG Microstate Analysis for Diagnosis of Children with ASD

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第42届中国控制会议

作者： Xiaoli Kong Jing Li Gaoxiang Ouyang School of Computer Science and Engineering Tianjin University of Technology State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University

The analysis of electroencephalogram(EEG) based on machine learning methods has been an effective tool for the diagnosis of children with autism spectrum disorders(ASD).In this paper,we introduce the microstate analysis of EEG to explore whether microstate features can serve as effective biomarkers for ASD *** preprocessing raw EEG data,we generate template data by aggregating the EEG data of all the ***,we segment the template data into five microstate prototypes,and use them to backfit all the preprocessed EEG data to generate a microstate ***,we compute microstate metrics and use various classifiers for the *** experimental results show that the features extracted by EEG microstate analysis can distinguish between ASD children and typically developing children more effectively.

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Identification of Beef Freshness Using Linear Discriminant Analysis (LDA) Algorithm and Extraction of HSV Values 2

Identification of Beef Freshness Using Linear Discriminant A...

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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Napianto, Riduwan Tri Prastowo, Agung Deni Wahyudi, Agung Wajiran Yudoutomo, Yohanes Egi Pratama Suparto, Arico Ayani Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Information Technology Education Study Program Stkip Pgri Situbondo Situbondo Indonesia

ISBN: (纸本)9781665465410

To determine the quality of beef, it is usually seen using a flashlight and matching it with a predetermined color standard. However, this method is time consuming and the results are inconsistent due to human visual limitations. This study aims to identify the freshness of beef based on its color characteristics using feature extraction method with HSV and LDA method for identification. The identification process begins with the transformation process from RGB images to L∗a∗b, then image segmentation serves to separate one object from another and image identification uses the LDA method with feature extraction using HSV. The use of color feature extraction with the HSV algorithm to assist in obtaining information from the colors contained in the image for easy identification. Furthermore, the LDA algorithm has a function to obtain the maximum projection to obtain a smaller dimensional space so that the desired patterns can be separated through the boundary line of a linear equation so as to form a certain class or group. Based on the test results using the confusion matrix, it produces a precision value of 80%, recall of 84%, and accuracy of 82%. © 2022 IEEE.

关键词： Beef

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A Systematic Integrated Literature Review of Clinical Data Management Platforms of HL7 FHIR 24

A Systematic Integrated Literature Review of Clinical Data M...

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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： Walter Viktor Walter-Tscharf, Franz Frederik Fondazione Bruno Kessler University of Trento European Institute of Innovation and Technology Doctoral Program Industrial Innovation Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350319934

This study is dedicated to a critical investigation of the existing approaches toward a data visualization management platform based on Fast Health Interoperability Resources (FHIR). Despite the growing popularity of FHIR, the available evidence provides a compelling reason to believe that its use is still accompanied by a significant number of technical challenges. Therefore, the development of tools to ensure its convenient visualization remains a pressing need for a variety of stakeholders operating in the healthcare industry. Results of the systematic literature review that included 26 studies provide valuable information about the most popular instruments in this area, such as the OpenEHR CKM approach. The review offers a critical discussion of the most important advantages and disadvantages of different approaches. It provides valuable recommendations for further research as well as practical recommendations concerning the best ways to utilize data visualization platforms based on FHIR. It defines the tradeoff of functionality versus the ease of dissemination as one of the most important problems facing most platforms and suggests avenues for developing additional tooling support for visualizing FHIR. The results of the research could be useful both for scientists and practitioners. © 2022 IEEE.

关键词： Data visualization

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Audiovisual Speaker Separation with Full- and Sub-Band Modeling in the Time-Frequency Domain

Audiovisual Speaker Separation with Full- and Sub-Band Model...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vahid Ahmadi Kalkhorani Anurag Kumar Ke Tan Buye Xu DeLiang Wang Department of Computer Science and Engineering Ohio State University USA Meta Reality Labs USA Center for Cognitive and Brain Sciences The Ohio State University USA

We introduce a new deep learning model for talker-independent audiovisual speaker separation in noisy conditions in the time-frequency domain. The inputs to the model include noisy multi-talker mixtures and the corresponding cropped face images. Our approach incorporates cross-attention audiovisual fusion, effectively merging audio and visual features and enabling seamless information interchange between auditory and visual modalities. These fused features drive a separator module, which separates the acoustic features of individual speakers. The separator module is based on the recently proposed TF-Gridnet, which comprises an intra-frame full-band component, a sub-band temporal module that captures frequency-specific temporal dependencies, and a cross-attention module dedicated to extracting long-term fused audiovisual features. To encourage the utilization of visual streams during training, we employ a Signal-to-Noise Ratio (SNR) scheduler. Experimental results demonstrate that the proposed model advances the state-of- the-art speaker separation performance in several audiovisual benchmark datasets.

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Providing Ancillary Services with Cellular Networks Leonardo Dias, Brigitte Jaumard, Lackis Eleftheriadis

SSRN

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SSRN 2024年

作者： Jaumard, Brigitte Dias, Leonardo Eleftheriadis, Lackis Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia University Canada Cognitive Automation Lab Ericsson Research Stockholm Sweden

关键词： Base stations

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A Conflicting Evidence Fusion Method Based on a Greedy Strategy

A Conflicting Evidence Fusion Method Based on a Greedy Strat...

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computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Haibo Li Jianyu Xiao Qi Tang School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

To address the issue of the conventional D-S combination rule not effectively dealing with highly conflicting evidence fusion, a conflicting evidence fusion algorithm utilizing a greedy strategy is proposed. First, by adopting a greedy strategy, the effective evidence distance is defined based on Jousselme’s evidence distance, followed by the definition of strongly and weakly correlated evidence. The average value of the effective evidence distance is calculated, and based on this the evidence is classified. Finally, weights are calculated individually for the classified evidence. Weights are then reassigned to the two types of evidence, and the corrected evidence is fused to obtain the final fusion result. Numerical examples are provided to demonstrate the effectiveness of the method proposed in this paper in addressing the limitations of the conventional D-S combination rule for fusing highly conflicting evidence.

关键词： computer vision Correlation Evidence theory Classification algorithms

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