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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Performance of Machine Learning Classifiers for Malware Detection Over Imbalanced Data

Performance of Machine Learning Classifiers for Malware Dete...

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Intelligent systems Conference (IntelliSys)

作者： Morillo, Paulina Bahamonde, Diego Tapia, Wilian Univ Politecn Salesiana Comp Sci Engn Quito Ecuador Univ Politecn Salesiana IDEIAGEOCA Res Grp Quito Ecuador

ISBN: (纸本)9783031477201;9783031477218

Detecting malware is crucial to avoid severe damage to a computer system. However, doing it by training Machine Learning algorithms can present complications since often there is imbalanced data. Therefore, one of the challenges faced by binary classification is learning to clearly distinguish between two classes when you have a much larger number of instances of one class than another. To decrease bias and to handle imbalance, some techniques increase or reduce the number of cases of the minority and majority classes, respectively. This paper analyzes the performance of three cost-sensitive classifiers, LR, DT, and RF, trained with an imbalanced malware detection dataset and four artificial datasets built using Near Miss, SMOTE, SMOTEENN, and SMOTE-Tomek re-sample techniques. The results show that Near Miss achieves a proper balance between the classes so that the algorithms increase their overall performance, reaching balanced accuracies greater than 95%. On the other hand, the rest of the techniques slightly increase the ability of the classifiers to identify objects of the minority class. Meanwhile, Random Forest achieved balanced and high performance. Besides, the training and testing times for oversampling or hybrid techniques are far superior to those obtained by undersampling since the latter reduces the number of instances processed by the models.

关键词： Binary Classification Re-Sample Oversampling Undersampling Hybrid Balance Accuracy G-Mean AUC Confusion Matrix

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Artificial Intelligence in Detecting Signs of Depression Among Social networks Users

Artificial Intelligence in Detecting Signs of Depression Amo...

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Intelligent systems Conference

作者： Gorrab, Abir Bonnerot, Thomas Univ Manouba Natl Sch Comp Sci RIADI Lab Manouba Tunisia Univ Paris1 Pantheon Sorbonne Ctr Rech Informat Paris France

ISBN: (纸本)9783031664304;9783031664311

Mental health has become more than just a concern in recent years, especially depression. Social networks are expanding to such an extent that they have become a platform for assessing mental health based on the analysis of messages and comments from different users. By analyzing social data, we will be able to detect depressive messages. To meet this need for text analysis, it appears that the use of intelligent systems and more particularly Artificial Intelligence is an ideal solution for detecting potential victims. However, the solutions provided are relatively recent and the results vary according to the methodologies employed. Several steps seem also to be essential in building an efficient framework to detect signs of depression in social media users. Thus, in this paper, we present the different phases that a depression detection framework from social data should respect. We then conduct a review comparing existing works in this field. This comparative study highlights the techniques used in every phase. By analyzing research works dealing with this issue, we also aim to understand the way in which Artificial Intelligence (AI) is used, identifying the advantages and limitations of existing works.

关键词： Artificial intelligence Machine learning Deep learning Depression detection Social networks Sentiment analysis

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Intralogistics in Learning Factories 14th

Intralogistics in Learning Factories

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14th International Conference on Learning Factories (CLF)

作者： Leber, Patrick Wengle, Markus Kevadiya, Gautam Dalm, Kris IWT Wirtschaft & Tech GmbH Fallenbrunnen 1 D-88046 Friedrichshafen Germany Ostbayr TH OTH Amberg Weiden Hetzenrichter Weg 15 D-92637 Weiden Germany

ISBN: (纸本)9783031654107;9783031654114

The "Lernfabrik Fallenbrunnen" is a privately funded Learning Factory, located in Friedrichshafen, Germany. The main research areas are collaborative robotics, machine vision, extended reality, and connectivity in production. Part of the learning factory is the manufacturing process of a demonstrator product, distributed to various places, including manual workstations with digital guidance, stationary and mobile robots, and an automated rack storage. This paper will focus on the intralogistics between the various places, using autonomous mobile robots (AMR) to transport pre-manufactured and finished products between working stations automatically, as well as stationary robots to load and unload components from the AMR's. The base components for the manufacturing process are delivered from outside the building to the first workstation, using an outdoor capable Husky AMR. The first workstation is an AR-guided manual workplace, where the worker picks these components directly from the Husky AMR. Further components are stored in boxes and are mounted by the worker. The pre-manufactured product is subsequently put on an ezWheel AMR. Using a camera, the load of the ezWheel is constantly checked for quantity and correctness, and when fulfilled, the ezWheel is automatically sent to the next station. There the pre-manufactured products will be picked from the ezWheel automatically on a conveyor belt system, using a stationary robot from KUKA. Further workstations are part of this conveyor belt system, and when the product is circulated once, it's finished and lifted from the KUKA robot to an automated high rack storage.

关键词： Learning Factory AMR Manufacturing ROS MES

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Review on Cybersecurity and Techniques 8th

Review on Cybersecurity and Techniques

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Pandya, Jay J. Kharote, Prashant SVKMs Narsee Monjee Inst Management Studies NMIMS Mukesh Patel Sch Technol Management & Engn Dept Informat & Technol Engn Mumbai 400056 Maharashtra India SVKMs Narsee Monjee Inst Management Studies NMIMS Mukesh Patel Sch Technol Management & Engn Dept Elect & Telecommun Engn Mumbai 400056 Maharashtra India

ISBN: (纸本)9789819713226;9789819713233

As we all know, in today's time, the world has become increasingly reliant on a variety of electronic gadgets for a variety of activities such as e-transactions, e-shopping, and so on. Cybersecurity assessments are critical for ensuring that critical cyber assets are properly safeguarded from attacks. Along with this, it is crucial to comprehend and be informed about cybercrime and cybersecurity. One should have a basic understanding of what cybersecurity is and the many techniques it uses. Hackers or a professional with fraudulent intent can easily get across any system to obtain crucial access and can also manipulate the system by attaching different types of malwares that can either shut down an organization's entire system or an individual's, putting them at risk of losing millions and millions of rupees. Here, we've covered a wide range of issues that internet user's encounter as well as many potential solutions in an effort to raise awareness of the latest developments in the field of cybersecurity. Examples include cybersecurity domains, approaches taken to deal with frequent instances of cybercrime, the CIA model, and more. The major goal of this study is to bridge this gap by thoroughly finding and analyzing cybersecurity assessment methodologies mentioned in scholarly literature.

关键词： Cyber ethics CIA model The ARPANET and the creeper

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A Drug-Virus Prediction Model Based on an Ensemble Classifier with Feature Optimization: A Case Study with COVID-19 5th

A Drug-Virus Prediction Model Based on an Ensemble Classifie...

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Aruna, A. S. Babu, K. R. Remesh Deepthi, K. APJ Abdul Kalam Technol Univ Govt Engn Coll Palakkad Dept Informat Technol Palakkad 678633 Kerala India Coll Engn Vadakara Dept Comp Sci Kozhikode 673105 Kerala India Cent Univ Kerala Govt India Dept Comp Sci Kasaragod 671320 Kerala India

ISBN: (纸本)9789819720521;9789819720538

Effective therapeutics are still unavailable for the contagious COVID-19, which originated in Wuhan in December 2019. It is induced by the newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This worldwide outbreak has taken many lives and disordered the world economy. Even though COVID-19 vaccines helped to boost immunity against the virus, an effective medication is still needed to curtail the disease outbreak. Drug repositioning is the process of investigating and finding novel uses for existing drugs in the market. It is an economical and time-saving solution to fight against this rapid and widespread infection. This work, ESRVDA aims to explore and predict Drug-Virus Associations to rank drugs against COVID-19 through an ensemble of Extreme gradient boosting classifier with Synthetic minority oversampling technique (SMOTE) and feature optimization by Recursive feature elimination cross-validation (RFECV). We propose a model to predict drugs for COVID-19 and other viral tscore is 0.9087. Case studies and experimental results and prove the efficacy of the model in the drug-virus association *** diseases through a machine learning-based method. We constructed feature vectors from the chemical structure similarity of drugs and the genomic sequence similarity of viruses. The optimized augmented samples are classified based on the ensemble classifier, and drugs are ranked based on the attained association probabilities. We conducted fivefold cross-validation to assess the performance of the model. The achieved AUC

关键词： COVID-19 Drug repositioning SARS-CoV-2 XGBoost Feature optimization

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Multiprocessing Performance Analysis Using Numerical Integration Techniques

Multiprocessing Performance Analysis Using Numerical Integra...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Yadav, Kartikey Kothari, Soham Prajapat, Shaligram Int Inst Profess Studies DAVV Indore India

ISBN: (纸本)9783031744426;9783031744433

Weddle's rule is one of the best for high-order numerical integration solving techniques. For better accuracy, more step sizes are used to compute the end result, but this leads to an increased number of computations. Parallel and multicore computations help us better enhance this computation. To maximize the performance ofWeddle's rule on contemporary processors, this research will investigate the role of multiprocessing. Utilizing multiprocessing and parallel computing techniques can significantly enhance the efficiency, accuracy, and scalability of these computations. In theory, this should reduce the computation time of the integration process as the workload is divided among multiple processors, resulting in better performance. Despite the theoretical promise of reduced computation times through multiprocessing, practical applications often reveal a discrepancy between expected and actual performance gains. This study analyzes the time consumed in computation over the number of processors (1 to 12) and the number of subintervals used in computation during the execution of the integration process. Also, the machine learning technique Gradient Descent can be used to predict time consumption using features like time taken, input size, and number of processors. The model has a 95% accuracy.

关键词： Numerical integration Weddle's rule Gradient descent Multi processing Multi-core performance

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Fuzzy Performance Measurement: A Literature Review 1

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Oztaysi, Basar CevikOnar, Sezi Cebi, Selcuk Kahraman, Cengiz Istanbul Tech Univ Dept Ind Engn Dept TR-34367 Istanbul Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkiye

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

Performance measurement (PM) is essential for assessing organizational efficacy, employing various methodologies and frameworks. In the literature, performance measurement is handled from two perspectives in the first group, effects of performance and factors affecting the organizational performance is analyzed. The second group focus on quantitative analysis which focus on tools and techniques which can be used in performance measurement. This paper examines the utilization of fuzzy sets in performance measurement studies, drawing from a literature survey conducted on Scopus. The analysis reveals a predominant use of ordinary fuzzy sets, with some studies employing extensions such as type-2 fuzzy sets and intuitionistic fuzzy sets. Furthermore, the paper delves into the methodologies employed in fuzzy performance measurement, highlighting Analytic Hierarchy Process and Data Envelopment Analysis as prominent approaches. By synthesizing these findings, the paper contributes to understanding the landscape of fuzzy-based performance measurement and underscores its significance in contemporary management practices.

关键词： Performance Measurement Fuzzy Sets multi-criteria decision making

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Vision-Based Multi-detection and Tracking of Vehicles Using the Convolutional Neural Network Model YOLO

Vision-Based Multi-detection and Tracking of Vehicles Using ...

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Intelligent systems Conference (IntelliSys)

作者： Moaga, Mpho Chunling, Tu Owolawi, Pius Tshwane Univ Technol Dept Comp Syst Tshwane South Africa

ISBN: (纸本)9783031477232;9783031477249

Vehicle detection and tracking play a critical role in Intelligent Traffic systems (ITS) for managing, identifying, and understanding the behaviour of vehicles on the road. In recent years, researchers have focused on developing computer vision-based approaches to improve the accuracy of vehicle detection and tracking. However, state-of-the-art approaches face challenges such as uncontrollable environmental conditions, occlusion of vehicles in a single frame, and high levels of noise, which can affect the accuracy of vehicle detection. In this study, we propose a computer vision-based approach to identify and track the movement of vehicles using the "YOU ONLY LOOK ONCE" (YOLO) version 7 Convolutional Neural Network (CNN) model. The YOLO model is fine-tuned to detect different types of vehicles, including cars, ambulances, buses, trucks, motorcycles, and bicycles. We utilized the Open Images Dataset from Google to test and validate our approach. Our experimental results demonstrate that the proposed approach achieves a higher detection accuracy of 81.28% mean Average Precision (mAP) compared to the state-of-the-art approaches. Furthermore, we tested our approach on the DAWN dataset to demonstrate its effectiveness as a multi-detector of vehicles in different weather conditions and online videos on the road for vehicle detection and tracking. Despite the challenges in the state-of-the-art computer vision-based ITS, our proposed approach demonstrates that it is possible to achieve higher accuracy using the YOLO version 7 CNN model. This study highlights the importance of developing accurate and efficient vehicle detection and tracking techniques to improve the operational functions of Intelligent Traffic systems.

关键词： Computer Vision Deep Learning Convolutional Neural Network Artificial Intelligent Deep Leaning Vehicle Detection Vehicle Tracking Intelligent Traffic System YOLO

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Leveraging Azure Data Factory for COVID-19 Data Ingestion, Transformation, and Reporting 8th

Leveraging Azure Data Factory for COVID-19 Data Ingestion, T...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Kulkarni, Radhika V. Jagtap, Vedant Naik, Tanaya Shaha, Shraddha Nikumbh, Khushi Vishwakarma Inst Technol Pune Maharashtra India

ISBN: (纸本)9789819713257;9789819713264

The COVID-19 pandemic has necessitated the collection and analysis of large volumes of data from various sources to understand its impact and make informed decisions. This research paper explores the utilization of Azure Data Factory, a managed cloud solution, for integrating and reporting COVID-19 data. The study focuses on two primary objectives. Firstly, it establishes a data platform using Azure Data Factory to enable data scientists to leverage machine learning models for predicting the spread of the virus and gaining insightful findings. This work demonstrates Azure Data Lake Gen2 as a reliable storage repository for ingesting and storing data. Furthermore, it explores the data transformation and analysis capabilities of Azure Data Factory using Data Flows, HDInsight, and Azure Databricks. The second objective revolves around the creation of a reporting platform for data analysts. This study illustrates how Azure Data Factory orchestrates the extraction, transformation, and loading of selected COVID-19 data into a SQL database. This subset of data is then leveraged to develop a Power BI report, enabling data analysts to visualize and report COVID-19 trends efficiently. Through a comprehensive analysis and practical implementation of Azure Data Factory, this research highlights its efficacy in handling COVID-19 data integration and reporting tasks. It sheds light on the data ingestion, transformation, and storage options provided by Azure Data Factory. It also showcases the seamless integration with other Azure services, including Data Flows, HDInsight, Azure Databricks, SQL databases, and Power BI.

关键词： Azure data factory (ADF) COVID-19 data analysis Databricks Data flows Data ingestion Data transformation HDInsight SQL database Power BI

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Several Online Pharmacies Leak Sensitive Health Data to Third Parties 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Carlsson, Robin Rauti, Sampsa Mickelsson, Sini Makila, Tuomas Heino, Timi Pirjatanniemi, Elina Leppanen, Ville Univ Turku Turku Finland Abo Akad Univ Turku Finland

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

As the demand for digital services keeps growing, online pharmacies have become a very important part of essential digital services. When sensitive personal data such as medicine orders are processed, privacy issues become increasingly important. In this paper, we take a look at personal data delivered to third parties in 20 Finnish online pharmacies. More specifically, we study whether the data on prescription medicine orders is leaked out to third-party analytics services. Our findings reveal that 14 (70%) of studied pharmacies send out information about the customers intending to order prescription medicines to third parties, and 7 (35%) of the pharmacies even leak the data about the specific prescription medicine a specific customer is ordering. We also discuss implications of the data leaks and give suggestions on how this alarming state of affairs can be alleviated.

关键词： Online pharmacies Web privacy Network traffic analysis Data concerning health

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