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Multi-RAT Fiber-Wireless Technologies towards 6G Networks 23

Multi-RAT Fiber-Wireless Technologies towards 6G Networks

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23rd International Conference on Transparent Optical Networks, ICTON 2023

作者： Vagionas, Chris Maximidis, Ronis Kanta, Konstantina Toumasis, Panagiotis Giannoulis, Giannis Apostolopoulos, Dimitris Kalfas, George Gatzianas, Marios Mesodiakaki, Agapi Avramopoulos, Hercules Miliou, Amalia Pleros, Nikos Aristotle University of Thessaloniki CIRI Dep. of Informatics 54636 Greece National Technical University of Athens School of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350303032

At the dawning of the beyond 5G era, broadband last mile connectivity is stimulating rapid developments in optical and wireless technologies that will efficiently handle the traffic generated by mobile users and demanding network applications. To this end, multiple Radio Access (multi-RAT) technologies operating at various millimeter wave (mmWave) frequency bands, e.g., V-/E-/D-band are being actively pursued, in parallel with the investigation of efficient fiber wireless fronthaul schemes, e.g. Analog Radio over Fiber (aRoF) or Intermediate Frequency over Fiber (IFoF), to deliver low-complexity, broadband, last-mile connectivity. However, integration of such novel technologies in an actual mobile network infrastructure remains a challenge, as it needs to support compatibility with the network operators' equipment. The current talk aims to present a holistic Fiber Wireless (FiWi) connectivity, demonstrating the co-existence of Analog and Digital RoF traffic along with flexible, reconfigurable point-to-multipoint connectivity to multi-RAT antenna units. Experimental results on high capacity FiWi fronthaul links operating in the V- (60 GHz) and D- (145 GHz) will be presented. Moreover, end-to-end, integrated real-time traffic and network operation will also be discussed and experimentally presented, indicating a possible technology roadmap towards multi-RAT 6G networks. © 2023 IEEE.

关键词： Radio-over-fiber

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Wrapper-Based Feature Selection to Improve The Accuracy of Intrusion Detection System (IDS) 10

Wrapper-Based Feature Selection to Improve The Accuracy of I...

引用

10th International Conference on Wireless and Telematics, ICWT 2024

作者： Jupriyadi Budiman, Arief Hamidi, Eki Ahmad Zaki Ahdan, Syaiful Negara, Ridha Muldina Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Gadjah Mada Department Computer Science and Electronics Yogyakarta Indonesia UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

An Intrusion Detection System (IDS) is a system that can detect attacks on a network. IDS systems can be built using machine learning approaches. However, significant network traffic and many features cause machine learning algorithms to work slowly. Not all available features are essential for use as a reference in an intrusion detection system. Feature selection is important because it can reduce the dataset and speed up computing time in building models and detection systems. In this paper, feature selection using the wrapper method is proposed. Its performance is compared with filter-based feature selection, such as chi-square, information gain, gain ratio, and correlation-based feature selection (CFS). Based on the exploration, the results showed that the feature selection algorithm using the wrapper method was superior to other algorithms. The number of critical features discovered and the detection system's precision serve as proof of the wrapper approach. With only three features as a reference, the detection system produces an accuracy of 96.22%. It increases 5.84% from 90.38% to 96.22%. Computation time also increases because the number of features used is tiny compared to other algorithms and uses all features. © 2024 IEEE.

关键词： Network intrusion

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Source Code Statement Classification using ANTLR and Random Forest 24

Source Code Statement Classification using ANTLR and Random ...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

In software development, source code analysis is essential to maintain software. One of the metrics used to assess software quality is software complexity, which can be calculated by analyzing source code statements like declaration, expression, and control. However, analyzing source code statements using an existing popular technique, like NLP, requires another structured form of data, which can be obtained by converting the source code into a token stream using a programming language processor. This study aims to get the best source code statement classification from six observed models and two source code forms. The models are Decision Tree, Naïve Bayes, SVM, kNN, Rocchio Algorithm and Random Forest. The source code forms are the raw source code and token stream object. The dataset collection used 562 rows of source code statements from an open-source Java project in a public repository. Next, we use ANTLR to transform the raw source code and get the token stream object. Later, the dataset was modeled using TF-IDF as an NLP technique to get the source code features. Then, six machine learning models were built and evaluated as a comparison for the classification process. As a result, Random Forest became the best model with the highest accuracy values among other machine learning models. Moreover, the token stream became the best object over source code as a contribution from the ANTLR. The model successfully predicted source code statements with a 96.1% accuracy score using the Random Forest model and ANTLR tools. In advance, this multiclass classification was also evaluated and gave a result. No declaration statement was predicted as a control statement, and vice versa (100% accuracy), but some miss prediction was observed in declaration-expression and expression-control pairs. Nevertheless, the Random Forest model's high accuracy and precision make it suitable for classifying source code statements. © 2023 IEEE.

关键词： Decision trees

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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Optimizing the Performance of Sentiment Analysis on Social Media Data Through Feature Selection in Preprocessing Techniques 6

Optimizing the Performance of Sentiment Analysis on Social M...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Yusnida, Yuyun Lase Darwis, R.A. Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Politeknik Negeri Medan Department of Mechanical Engineering Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

The digital era directly builds user needs in communicating using social media, the data generated by social media has a large volume so it needs to be analyzed with a preprocessing process. The problems that exist in social media data contain a lot of repeated words with non-standard sentence structures and still contain lots of irregular punctuation making it difficult to determine which features are considered important in a sentence to carry out a sentiment analysis process in identifying positive, negative, and neutral sentences. So it is necessary to select features using preprocessing techniques using Recursive Feature Elimination (RFE). This study produces a comparison that shows that the selection of features and combinations in using features results in the accuracy of each algorithm increasing when compared to using one feature. The results showed that the CNN algorithm has an accuracy value of 0.95, the LSTM algorithm is 0.94 and the RNN algorithm is 0.93. when compared to the use of one feature, it increases by 0.09 for the CNN algorithm, increases by 0.07 for the LSTM algorithm, and increases by 0.05 for the RNN algorithm. © 2023 IEEE.

关键词： data feature selection optimizing preprocessing

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An Augmented Reality Environment for Testing Cockpit Display Systems 7th

An Augmented Reality Environment for Testing Cockpit Displa...

引用

7th International Conference on computer-Human Interaction Research and Applications, CHIRA 2023

作者： Potur, Caner İnce, Gökhan MGEO Test and Verification Directorate ASELSAN Inc. Ankara Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031493676

In the avionics industry, software test automation is a crucial component, as it plays a significant role in accelerating the development process. Since all automation systems are error-prone, the automation systems that affect flight safety should be supervised by humans as they must be reliable. In this study, an augmented reality based software verification system is designed to observe the visual verification of cockpit screens and detect possible automation errors. In the designed system, an observer can instantly see the test steps and automation results using an augmented reality headset as a hologram on the cockpit screen and can report the errors they observe in test automation. The performance of the proposed system was analyzed qualitatively and quantitatively. The experimental results demonstrated that the use of augmented reality in cockpit display verification systems accelerated the testing process, reduced users’ cognitive load, and improved the system’s usability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Chest Radiographs Enhancement with Contrast Limited Adaptive Histogram 17

Chest Radiographs Enhancement with Contrast Limited Adaptive...

引用

17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Lee, Hyeri Choo, Hyunseung Le, Duc-Tai Bum, Junghyun Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781665453486

Among all the image contrast enhancement methods, histogram equalization is the most widely used. However, conventional histogram equalization causes the entire image to be too bright, resulting in noise generation as a consequence of excessive brightness changes and information loss when the histogram is intensively distributed into a specific amount of brightness. The Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is designed to address these limitations. To avoid overamplifying brightness variations, CLAHE clips the histogram of an input image to make it uniformly distributed. This study employs the CLAHE algorithm to improve contrast in chest radiographs, with the expected result that lung disease diagnosis will be more accurate. © 2023 IEEE.

关键词： Image reconstruction

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Effect of CLAHE-based Enhancement on Bean Leaf Disease Classification through Explainable AI 12

Effect of CLAHE-based Enhancement on Bean Leaf Disease Class...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Noviandy, Teuku Rizky Maulana, Aga Khowarizmi, Faris Muchtar, Kahlil Universitas Syiah Kuala Artificial Intelligence Department of Informatics Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Aceh Indonesia

ISBN: (纸本)9798350340181

Global agriculture faces a major threat from plant diseases, particularly those affecting bean leaves, resulting in significant crop yield losses and impacting farmers' livelihoods and food production. To overcome the limitations of manual visual inspection methods, researchers have adopted computer vision and deep learning techniques for automated disease detection. This study improves the accuracy of classifying bean leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the EfficientNetB0 architecture. Explainable AI technique, specifically Grad-CAM, is employed to analyze predicted images for transparency and interpretability. The proposed approach outperforms models trained on original images, achieving higher accuracy, precision, recall, and F1 score. CLAHE proves to be effective in optimizing CNN-based models for plant disease detection. Grad-CAM analysis reveals the model's focus on disease-related features, enhancing understanding of its behavior. This research contributes to the development of robust automated systems for plant disease detection, benefiting farmers and agricultural practitioners in decision-making. © 2023 IEEE.

关键词： Deep learning

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Causal Effects of Environmental Exposures and Biological Traits on the Difference between Phenotypic and Chronological Ages

Causal Effects of Environmental Exposures and Biological Tra...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Pala, Daniele Xie, Yuezhi Xu, Jia Zhang, Yuqin Shen, Li University of Pavia Department of Computer Electrical and Biomedical Engineering Pavia Italy University of Pennsylvania Department of Biostatistics Epidemiology and Informatics Philadelphia United States

ISBN: (纸本)9798350337488

Aging is a physiological process associated with numerous cardiovascular, degenerative and neurological conditions. The current demographic changes in the world, characterized by an increasing average age especially in Europe and North America, are causing an increment in the prevalence of such conditions, leading to a public health problem as more resources are required to manage treatments. Recent research has defined a new score named Phenotypic Age, i.e. an adjusted age that takes into account the current health status considering a series of biomarkers, that can be useful to provide a more precise estimation of the probability of developing aging-related conditions. Prevention of such conditions can be performed more efficiently by studying the mechanisms that lead to an increased phenotypic age rather than attempting to treat a patient that has already a high health risk. In this work, we combine pairwise association techniques and mediation analysis to define a strategy to investigate the inner causal mechanisms that lead from specific environmental exposures to an increasing gap between phenotypic and chronological age, considering the influence of biological variables. Four environmental exposures and 11 biological traits have been identified in the NHANES dataset, and each trait has been tested as a mediation variable for each exposure. Almost all mediations reported significant indirect effects with specific results that provide new insights into the causal mechanisms that lead to a deranged aging rate. © 2023 IEEE.

关键词： Aging Exposome Mediation Analysis Phenotypic Age Public Health

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