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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Srinivasan, R. Mallikarjuna, Basetty Kavitha, M. Kavitha, R. Naib, Baharat Bhushan VelTech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Dept. of CSE Chennai India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida203 201 India

ISBN: (纸本)9781665437899

The Internet of Things (IoT) stands a novel concept which enables Internet connectivity to everyone kind of things and people. IoT integration of several long-distance, short-range, also wireless personal area technologies also networks into design and implementation of IoT applications. For IoT systems, the research community and industry have proposed numerous communication technologies. Any IoT device's communication is one of the most significant aspects The ability to transfer and share data between devices. Wireless communication is mostly used by IoT devices to communicate with one another. This enables many businesses opportunity in field as Cities that are'11 smart', smart homes, e-health, between numerous others. In this article provides the recently developed wireless technologies associated with IoT in a comprehensive manner. The study examines the impact of Wireless technologies with low power consumption on communication over a vast distance for internet of things applications. Several commercial openings are created Fields of study such as electronic health, Cities that are smart, and home automation. The IoT enables a number of emerging wireless technologies that have been identified in this study. Comparing In terms of wireless communications, there are a variety of options, architectures, structures, protocol structures, and security, this paper offers analysis. The study focuses on connectivity and challenges in each area. © 2022 IEEE.

关键词： Internet of things

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An Experiment on Feature Selection using Logistic Regression

arXiv

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arXiv 2024年

作者： Islam, Raisa Mazumdar, Subhasish Islam, Rakibul Dept of Computer Science & Engineering New Mexico Institute of Mining and Technology SocorroNM87801 United States

In supervised machine learning, feature selection plays a very important role by potentially enhancing explainability and performance as measured by computing time and accuracy-related metrics. In this paper, we investigate a method for feature selection based on the well-known L1 and L2 regularization strategies associated with logistic regression (LR). It is well known that the learned coefficients, which serve as weights, can be used to rank the features. Our approach is to synthesize the findings of L1 and L2 regularization. For our experiment, we chose the CIC-IDS2018 dataset [1] owing partly to its size and also to the existence of two problematic classes that are hard to separate. We report first with the exclusion of one of them and then with its inclusion. We ranked features first with L1 and then with L2, and then compared logistic regression with L1 (LR+L1) against that with L2 (LR+L2) by varying the sizes of the feature sets for each of the two rankings. We found no significant difference in accuracy between the two methods once the feature set is selected. We chose a synthesis, i.e., only those features that were present in both the sets obtained from L1 and that from L2, and experimented with it on more complex models like Decision Tree and Random Forest and observed that the accuracy was very close in spite of the small size of the feature set. Additionally, we also report on the standard metrics: accuracy, precision, recall, and f1-score. © 2024, CC BY-SA.

关键词： Feature Selection

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NB-IoT Preamble Signal: A Survey 7

NB-IoT Preamble Signal: A Survey

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： DIan, F. John Vahidnia, R. British Columbia Institute of Technology Dept. of Electrical and Computer Engineering Burnaby Canada

ISBN: (纸本)9781665444316

The Narrowband Internet of Things (NB-IoT) is a cellular IoT communication technology to address the IoT use cases where the smart devices can establish a connection with the network using Random Access (RA) process. The RA process begins with sending a preamble signal in uplink direction. In this paper, we explain about this signal and discuss the effect of wireless channel on the preamble signal. We model the preamble signal at the transmitter side as well as the receiver side. We also classify the research work performed on this signal to four different clusters and consequently explain the methods and techniques utilized in each cluster. © 2021 IEEE.

关键词： Internet of things

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Addressing Misinformation in Bengali Media: A Hybrid Deep Learning Solution

Addressing Misinformation in Bengali Media: A Hybrid Deep Le...

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International Conference on computer and Information technology (ICCIT)

作者： Fuad Ibne Jashim Farhad Shah Imran Md Mehedi Hasan Santo Mahbub Khan Anamul Sakib Md Shahidur Rahman Md Ariful Islam Rezaul Haque Shafiur Rahman Dept. of Information Technology CQ University Australia Dept. of Computer Science and Engineering BRAC University Bangladesh Dept. of Management Information System Pacific State University USA Dept. of Business Administration International American University USA Dept. of Engineering Management Westcliff University USA Dept. of Computer Science and Engineering East West University Bangladesh Dept. of Software Engineering Daffodil International University Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The spread of fake news poses significant threats to society, democracy, and public discourse. This is especially true in Bengali-speaking communities where digital literacy and language-specific resources are limited. To address this issue, we urgently need effective ways to detect and mitigate fake news in the Bengali language. This paper highlights the unique challenges associated with linguistic diversity and misinformation prevalence. Our research introduces a hybrid deep learning algorithm specifically designed to classify fake and authentic Bengali news articles. The methodology involves extensive data collection, rigorous preprocessing to improve textual quality, and the use of advanced feature extraction methods. We combined and trained CNN and LSTM/BiLSTM models to handle the nuances of Bengali text. Our proposed algorithm significantly outperforms existing models, with a high accuracy of 98.45% in distinguishing between fake and authentic news and reducing false positives. Additionally, we developed a user-friendly web application that allows general public to input news articles and obtain predictions on their authenticity in real-time. This study not only advances the field of fake news detection but also provides essential tools for journalists, policymakers, and the general public to combat misinformation in Bengali media. Our findings have broader implications, promoting informed public discourse and supporting the development of resilient digital ecosystems. This underscores the need for ongoing research and adaptation of such models to combat the global challenge of fake news in various languages and contexts.

关键词： Deep learning Machine learning algorithms Social networking (online) Biological system modeling Scalability Ecosystems Text categorization Classification algorithms Cultural differences Fake news

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Enhanced Cloud Security using Cryptography and Steganography Techniques

Enhanced Cloud Security using Cryptography and Steganography...

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2021 IEEE International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications, CENTCON 2021

作者： Bindu Madavi, K.P. Vijaya Karthick, P. Dayananda Sagar University Dept. Computer Science and Engineering Bangalore India Sir M. Visvesvaraya Institute of Technology Dept. Information Science and Engineering Bangalore India

ISBN: (纸本)9781665400176

Data security is a key concern for organizations considering a transfer of their on-premises applications to the cloud. Organizations must shift their security controls from historical perimeter and detection-based technologies to a focus on establishing enhanced protection at the application and data levels to ensure the confidentiality, integrity, and availability of these various systems and datasets. Data integrity is a critical component of cloud data security, preventing unauthorized alteration or removal and guaranteeing that data stays as it was when it was initially uploaded. This article presents a compacting with steganography technique which is used to hide data with substantial security and also perfect invisibility while utilizing a combination of DES, AES, and RC4 encryption methods. The objective of this study is to provide data security using steganography with the Least Significant Bit (LSB) Algorithm and Hybrid Encryption that encrypts user input and conceals it in an image file to provide the highest level of security for messages sent and received. © 2021 IEEE.

关键词： Steganography

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Estimating Substation Lightning Arrester Deterioration Guideline: A k-NN Algorithm with Leakage Current Measurement 13

Estimating Substation Lightning Arrester Deterioration Guide...

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13th International Conference on Power, Energy and Electrical engineering, CPEEE 2023

作者： Gil, Yujung Kim, Jihie Kim, Dongho Dongguk Univerisity Dept. of Computer Science and Engineering Seoul Korea Republic of Dongguk Univerisity School of Artificial Intelligence Convergence Seoul Korea Republic of Dongguk Univerisity Dongguk Institute of Convergence Education Seoul Korea Republic of

ISBN: (纸本)9781665490146

Lightning arresters are used as protective devices in substations to increase stability so that the surge can flow to grounding. Leakage currents can determine insulation deterioration of lightning arresters. This paper comes up with new guidelines for the lightning arresters from the manufacturers who do not provide deterioration guidelines by analyzing the data set KEPCO accumulated from the substations for the last 20 years. We used several expressions from previous research, cosine similarity, and the k-NN algorithm to obtain the result. We also used Robust Scaler to minimize the influence of outliers. © 2023 IEEE.

关键词： k-nearest neighbors

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Event-based Photometric Bundle Adjustment

arXiv

引用

arXiv 2024年

作者： Guo, Shuang Gallego, Guillermo The Dept. of Electrical Engineering and Computer Science Technische Universität Berlin The Robotics Institute Berlin Germany Berlin Germany

We tackle the problem of bundle adjustment (i.e., simultaneous refinement of camera poses and scene map) for a purely rotating event camera. Starting from first principles, we formulate the problem as a classical non-linear least squares optimization. The photometric error is defined using the event generation model directly in the camera rotations and the semi-dense scene brightness that triggers the events. We leverage the sparsity of event data to design a tractable Levenberg-Marquardt solver that handles the very large number of variables involved. To the best of our knowledge, our method, which we call Event-based Photometric Bundle Adjustment (EPBA), is the first event-only photometric bundle adjustment method that works on the brightness map directly and exploits the space-time characteristics of event data, without having to convert events into image-like representations. Comprehensive experiments on both synthetic and real-world datasets demonstrate EPBA’s effectiveness in decreasing the photometric error (by up to 90%), yielding results of unparalleled quality. The refined maps reveal details that were hidden using prior state-of-the-art rotation-only estimation methods. The experiments on modern high-resolution event cameras show the applicability of EPBA to panoramic imaging in various scenarios (without map initialization, at multiple resolutions, and in combination with other methods, such as IMU dead reckoning or previous event-based rotation estimation methods). We make the source code publicly available. © 2024, CC BY.

关键词： Least squares approximations

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Software Development Effort Estimation Using Regression Models Delve with Explainable Machine Learning (XAI)

Software Development Effort Estimation Using Regression Mode...

引用

Information technology (OCIT), OITS International Conference on

作者： Rahul Kumar Mandal Jagannath Singh Debolina Ghosh School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Dept. of Information Technology Manipal University Jaipur Jaipur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Software projects often fail due to poor effort estimation, which can lead to issues such as wasted time and resources. Developing software requires significant time, money, and skilled people, making accurate effort estimation essential. Recent research uses machine learning algorithms and datasets to improve estimation, but these models can be difficult to interpret. In this study, we utilize SHAP (an explainable machine learning technique) to examine the key variables that impact effort estimation. We apply four different regression models to the China, Maxwell, and Desharnais datasets: Linear Regression, Random Forest Regression, Support Vector Regression and Artificial Neural Network. Our results identify the key attributes affecting effort estimation in each dataset, providing valuable insights into the models and improving their transparency and reliability.

关键词： Support vector machines Machine learning algorithms Linear regression Estimation Artificial neural networks Software Software reliability Information technology Random forests Software development management

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Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

arXiv

引用

arXiv 2023年

作者： Akter, Mst Shapna Shahriar, Hossain Ahamed, Sheikh Iqbal Gupta, Kishor Datta Rahman, Muhammad Mohamed, Atef Rahman, Mohammad Rahman, Akond Wu, Fan Dept. of Computer Science Kennesaw State University Kennesaw United States Dept. of Information Technology Kennesaw State University Kennesaw United States Dept. of Computer Science Marquette University Wisconsin United States Dept. of Computer Science Clark Atlanta University GA United States Dept. of Information Technology Clayton State University Morrow United States Dept. of Information Technology Georgia Southern University Georgia Dept. of Electrical and Computer Engineering Florida International University Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real-world problems. QML has the potential to address cybersecurity-related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. © 2023, CC BY.

关键词： Support vector machines

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