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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Alhafizh, Muhammad Rifqi Adriawan, Albertus Baskara Yunandito Fernaldy, Darryl Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

Artificial intelligence (AI) refers to human-like intelligence exhibited by computers, robots, or other machines. In popular use, artificial intelligence refers to the ability of a computer or machine to mimic the ability of the human mind to learn from examples and experiences, recognize objects, understand and respond to language, make decisions, solve problems, and combine these and other abilities to perform functions that humans might perform. Artificial intelligence requires experience and data to smarten up the technology. The most important things in making artificial intelligence are learning, reasoning, and self-correction. At the learning stage, AI gives machines the ability to learn tasks without requiring a defined programming language. Then, the reasoning stage is the stage where the reason AI is applied in a technology. The self-correction stage is the stage where the AI is refining itself from and learning from experience in order to minimize errors or problems that exist. Then, this matter is concerned with the parking lot to be discussed. Parking can be interpreted as public facilities available in agencies or offices that serve to store vehicles. Vehicles entering the parking area become tens or even thousands, because it requires a parking system and management area. Such arrangements are capable of parking procedures and even other support systems such as adequate parking facilities and infrastructure, and another function is to create and develop parking systems in general to provide safety and comfort. The methodology obtained from this problem is checking the research model to check and find out the comparison of vehicle user’s Parking System Security between cars or motorcycles, as well as the usual parking lots visited. Then, we created a questionnaire. This study used questionnaires with the aim to get respondents’ results about the Parking Security System. Our sampling method is based on respondents aged 17–20 years. Questionnaires are su

关键词： Security systems

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The Evolution and Integration of Seamlessly Synchronized Data Streams in Modern Businesses 3

The Evolution and Integration of Seamlessly Synchronized Dat...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Acharjya, Kalyan Sharma, Sunil Ezhilarasan, G. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In today's age of data-driven decision making, it is crucial for organizations to keep all of their data streams in perfect sync. Research methods and findings from an investigation into 'The Evolution and Integration of Seamlessly Synchronized Data Streams in Modern Businesses' Data analytics, principal component analysis, k-means clustering, and autoregressive integrated moving average (ARIMA) are all included into the suggested technique. This all-encompassing method permits a thorough investigation into the development and incorporation of data synchronization in the present-day corporate environment. The results analysis demonstrates that the suggested approach outperforms the conventional ones. We compared the suggested strategy to six established alternatives. Data analysis, extensive analysis, mathematical modeling, practical insights, scalability, and overall performance are just a few of the areas where the suggested technique excels above conventional approaches. These distinctions are highlighted, and the data are represented robustly by four various forms of visualizations: scatter plots, pie charts, box plots, and heatmaps. The importance of synchronized data streams in contemporary enterprises is highlighted by our research, which highlights the need for a multidimensional and data-driven approach to this topic. The findings of this study may help businesses enhance customer service, save costs, and streamline operations by enhancing their data synchronization techniques. © 2023 IEEE.

关键词： Synchronization

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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic Engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer Chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Apple Variety Classification: Leveraging CNN and Random Forest for Precision 3

Apple Variety Classification: Leveraging CNN and Random Fore...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yashu Kukreja, Vinay Brar, Tejinder Pal Singh Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh Group of Colleges Department of Computer Application Mohali Landran India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350319125

An important difficulty in computer vision and agricultural technology is the automatic classification of apple cultivars based on their shape, texture, and colour properties. In this article, we suggest a unique method for tackling this problem that combines Random Forest classifiers and Convolutional Neural Networks (CNNs). In total, 5,510 high-resolution photos of the five well-known apple cultivars Gala, Cortland, Pink Lady, Honeycrisp, and Fuji were gathered in our extensive collection. In the first step of our process, a CNN automatically extracts distinguishing features from these photos. Four convolutional layers with ReLU activation make up the CNN architecture, which is followed by layers with maximum pooling to reduce spatial dimension. The Random Forest classifier then uses the output probabilities from the CNN to determine the final classification. We rigorously experimented to show the accuracy and resilience of our model in identifying these apple types. Deep learning and ensemble approaches work together to successfully capture minute variations in shape, texture, and colour. The agriculture and food industries stand to benefit significantly from this research in terms of automated quality control, precise inventory management, and increased customer satisfaction. Our method opens up new directions for future study in automated fruit classification and agricultural automation while demonstrating the potential of contemporary machine learning techniques to simplify difficult agricultural operations. © 2023 IEEE.

关键词： agriculture apple cnn-svm deep learning fruit type detection

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Investing in Products with the Greatest Demand on Online Stores During the Pandemic 7th

Investing in Products with the Greatest Demand on Online Sto...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Hassya, Titan Hanif, Muhammad Fauzi Alvian Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

From the year 2019, every aspect of our lives has been negatively impacted as a result of the spread of a deadly disease known as COVID-19, particularly the health and business domains. The primary goal of this study is to develop a solution, or at the very least an overall analysis, to the readers on how to overcome the economic crisis during pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Extracting Aspect Sentiment from Users’ Book Reviews for Recommendation

Extracting Aspect Sentiment from Users’ Book Reviews for Re...

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Computational Linguistics and Natural Language Processing (CLNLP), International Conference on

作者： Yachao Cui Peng Zhou Kang Li Bingfeng Seng Han Cao School of Computer Science Shaanxi Normal University Xi’an China Department of Computer Technology and Application Qinghai University Xining China

ISBN: (数字)9798350392098

ISBN: (纸本)9798350392104

When shopping for books on e-commerce platforms, other users’ evaluations of books contain a large number of users’ intuitive feelings, from which useful information can be mined to provide buyers with ideas for shopping. In terms of recommendation algorithms, although the existing research work can use various methods to model fine-grained user preferences to improve the recommendation effect. However, we believe there are still shortcomings. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools and are not necessarily applicable to the recommendation domain. Second, the features extracted from reviews are still potential features when incorporated into recommendation algorithms, and they cannot be fully utilized to characterize user embeddings. For this reason, this paper proposes a new user comment extraction scheme for personalized recommendation. We first use text summarization techniques in the field of NLP to preprocess and refine the comment text to reduce data noise and obtain high-quality text information. Subsequently, in order to more accurately characterize user embeddings and item features, we use advanced aspect-based sentiment analysis techniques to extract fine-grained aspectual items and corresponding sentiment polarity from the review text. Finally, experimental validation on a real book dataset is conducted to demonstrate the effectiveness of our model.

关键词： Sentiment analysis Accuracy Reviews Computational modeling Noise Text summarization Feature extraction Book reviews Electronic commerce

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MD-Dose: A diffusion model based on the Mamba for radiation dose prediction

MD-Dose: A diffusion model based on the Mamba for radiation ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fu, Linjie Li, Xia Cai, Xiuding Wang, Xueyao Shen, Yali Yao, Yu Chinese Academy of Sciences Chengdu Institute of Computer Application Chengdu China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China Sichuan University Radiophysical Technology Center Cancer Center West China Hospital China Sichuan University West China Hospital Department of Abdominal Oncology China

ISBN: (纸本)9798350386226

Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption. The code is publicly available at https://***/flj19951219/mamba-dose. © 2024 IEEE.

关键词： Diseases

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Gradient Sign Inversion: Making an Adversarial Attack a Good Defense

Gradient Sign Inversion: Making an Adversarial Attack a Good...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaojian Ji Li Dong Rangding Wang Diqun Yan Yang Yin Jinyu Tian Department of Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology Geely Auto Central Research Institute School of Computer Science and Engineering Macau University of Science and Technology

Deep neural networks have been proven vulnerable to deliberately crafted adversarial example, which cause serious safety and security concerns. Many defense approaches were proposed to resist such threats. However, existing defenses such as pre-compression or adversarial training would degrade the model performance on clean images or incur heavy computational costs. In this work, we propose a plug-and-play defensive module Gradient Sign Inversion (GSI) to defend gradient-based attack. Essentially, GSI attempts to inverse the direction of the backpropagated gradient for the victim model, disturbing the adversarial example generation of the attacking while retaining the performance of the vanilla network on genuine inputs. Specifically, an additive model based on periodic trigonometric function is established by investigating the necessary conditions that a suitable defensive module should have. By enforcing constraints on the defensive module, the parameters of GSI are determined, accompanied by a theoretical justification. Interestingly, we observe that the proposed GSI not only prevents the gradient-based adversarial attack, but can even improve the confidence of the ground-truth label when initiating an attack, making the attack betray as a defense. Source code is publicly available at https://***/JidaDiao/GSI.

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Detection of Arrhythmia from ECG Signal Using Bat Algorithm-Based Deep Neural Network 7th

Detection of Arrhythmia from ECG Signal Using Bat Algorithm-...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Singh, Kabrambam Rupabanta Dash, Sujata Atta-ur-Rahman Department of Computer Application Maharaja Srirama Chandra Bhanja Deo University Sriramachandra Vihar Odisha Baripada India DRIEMS School of Professional Studies Odisha Tangi Cuttack India College of Computer Science and Information Technology Imam Abdul Rahman Bin Faisal University Damian Saudi Arabia

ISBN: (纸本)9789819950140

Heart disease is one of the deathliest diseases in the world. As the ECG data is widely available, many machine learning and deep learning models have been developed for the automatic Arrhythmia diagnosis system. In this study, BiLSTM with the Bat algorithm has been proposed for the classification of the Normal ECG signal and the Arrhythmia ECG signal. It can detect the abnormality of the heart rhythm. The novelty of this study is the feature selection using the Bat algorithm that enhanced the performance of the classification model. The performance of this method is compared with the existing methods and it gives better performance than the other existing methods. With the proposed method, an accuracy of 99.75% has been achieved using tenfold cross-validation. It can help the risk patient in the early detection of the abnormality of the heart rhythm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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