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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjunatha S Swetha M D Rashmi S Ananda Kumar Subramanian Vinoth Kumar V Mallikarjuna Swamy S Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Computer Science and Engineering BNM Institute of Technology Bengaluru India Department of Computer Science and Engineering (Data Science) Dayananda Sagar College of Engineering Bengaluru India Dept of Computational Intelligence School of Computer Science Engineering VIT – University Vellore India School of Computer Science Engineering and Information Systems VIT – University Vellore India Dept of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution.

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation Network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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Comparative Effectiveness of Classification Algorithms in Predicting Diabetes

Comparative Effectiveness of Classification Algorithms in Pr...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Fares A. Dael D. Mareyev Ibraheem Shayea S. Kulniyazova Korlan Gulnara Abitova Management Information Systems İzmir Bakirçay University Īzmir Turkey Department of Computational and Data Science Astana IT University Astana Kazakhstan Electronic and Communication Engineering Istanbul Technical University Istanbul Turkey Department of Systems Analysis and Control L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Diabetes mellitus poses a significant global health challenge, with increasing prevalence, particularly in low socioeconomic regions. Accurate and early diagnosis is crucial to prevent the severe long-term complications associated with diabetes. This study conducts a comprehensive comparison of six prominent machine learning algorithms-K-Nearest Neighbors (K-NN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forest, and Logistic Regression-in predicting diabetes using a dataset of 768 individuals with diverse diabetic indicators from Kaggle. Each algorithm is rigorously evaluated based on precision, recall, and F1-score to determine the most effective method for diabetes diagnosis. The results indicate that Logistic Regression outperforms the other algorithms, achieving an accuracy of 81%. This superior performance is attributed to Logistic Regression's ability to effectively delineate linear separations, which is crucial for distinguishing between diabetic and non-diabetic individuals. The study underscores the importance of feature selection and model tuning in enhancing predictive performance. The findings suggest that integrating Logistic Regression into clinical settings can significantly improve the accuracy and timeliness of diabetes diagnosis, potentially leading to better patient outcomes and reduced healthcare costs.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Prediction algorithms Diabetes Classification algorithms Bayes methods Medical diagnostic imaging Regression tree analysis

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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When is the estimated propensity score better? High-dimensional analysis and bias correction

arXiv

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arXiv 2023年

作者： Su, Fangzhou Mou, Wenlong Ding, Peng Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences United States Department of Statistics UC Berkeley United States Department of Mathematics Lab for Information and Decision Systems and Statistics and Data Science Center Massachusetts Institute of Technology United States

Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this claim comes with several qualifications: it holds only if propensity score model is correctly specified and the number of covariates d is small relative to the sample size n. We revisit this phenomenon by studying the inverse propensity score weighting (IPW) estimator based on a logistic model with a diverging number of covariates. We first show that the IPW estimator based on the estimated propensity score is consistent and asymptotically normal with smaller variance than the oracle IPW estimator (using the true propensity score) if and only if n & d2. We then propose a debiased IPW estimator that achieves the same guarantees in the regime n & d3/2. Our proofs rely on a novel non-asymptotic decomposition of the IPW error along with careful control of the higher order terms. Copyright © 2023, The Authors. All rights reserved.

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Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective

arXiv

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arXiv 2023年

作者： Suo, Dajiang Jayawardana, Vindula Wu, Cathy The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University TempeAZ85281 United States The Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems Department of Civil and Environmental Engineering Institute of Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States

An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Therefore, in this work we study the CAV-assisted corridor clearance for EMS vehicles from a short term deployment perspective. Existing research on this topic often overlooks the impact of EMS vehicle disruptions on regular traffic, assumes 100% CAV penetration, relies on real-time traffic signal timing data and queue lengths at intersections, and makes various assumptions about traffic settings when deriving optimal model-based CAV control strategies. However, these assumptions pose significant challenges for near-term deployment and limit the real-world applicability of such methods. To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods. Our qualitative analysis highlights the complexities of designing scalable EMS corridor clearance controllers for diverse traffic settings in which DRL controller provides ease of design compared to the model-based methods. In numerical evaluations, the model-free DRL controller outperforms the model-based counterpart by improving traffic flow and even improving EMS travel times in scenarios when a single CAV is present. Across 19 considered settings, the learned DRL controller excels by 25% in reducing the travel time in six instances, achieving an average improvement of 9%. These findings underscore the potential and promise of model-free DRL strategies in advancing EMS response and traffic flow coordination, with a focus on practical near-term deployment. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Assessing LLMs in Malicious Code Deobfuscation of Real-world Malware Campaigns

arXiv

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arXiv 2024年

作者： Patsakis, Constantinos Casino, Fran Lykousas, Nikolaos Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Information Management Systems Institute of Athena Research Centre Greece Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Spain Data Centric Romania

The integration of large language models (LLMs) into various pipelines is increasingly widespread, effectively automating many manual tasks and often surpassing human capabilities. Cybersecurity researchers and practitioners have recognised this potential. Thus, they are actively exploring its applications, given the vast volume of heterogeneous data that requires processing to identify anomalies, potential bypasses, attacks, and fraudulent incidents. On top of this, LLMs’ advanced capabilities in generating functional code, comprehending code context, and summarising its operations can also be leveraged for reverse engineering and malware deobfuscation. To this end, we delve into the deobfuscation capabilities of state-of-the-art LLMs. Beyond merely discussing a hypothetical scenario, we evaluate four LLMs with real-world malicious scripts used in the notorious Emotet malware campaign. Our results indicate that while not absolutely accurate yet, some LLMs can efficiently deobfuscate such payloads. Thus, fine-tuning LLMs for this task can be a viable potential for future AI-powered threat intelligence pipelines in the fight against obfuscated malware. © 2024, CC BY-NC-SA.

关键词： Malware

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Iot Based Smart Assistant Cane For The Visually Impaired

Iot Based Smart Assistant Cane For The Visually Impaired

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： A Harini C Buvana M Harshini S Keerthana D Prabhu S K Umamaheswaran Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu Department of Information Technology Sri Sairam Engineering College Chennai Tamil Nadu Department of Artificial intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Tamil Nadu Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper presents the design and implementation of a Smart Cane for the visually impaired individuals, incorporating several sensors and features to improve their mobility and safety. The device includes an ultrasonic sensor for obstacle detection, an MPU-6050 device for detecting falls and tilts, a GPS module for navigation, an RTC module for medicine reminders, and a fitness tracking module for monitoring health parameters. Additionally, a Bluetooth headset with a microphone is provided to communicate with the voice assistant in noisy areas. The Smart Cane triggers a buzzer for help and sends a notification to the caretaker in case of a fall or crash. The device is powered by a LIPO battery and is designed to improve the quality of life of visually impaired individuals by providing them with enhanced mobility and safety features.

关键词： Navigation Personal voice assistants Sensor phenomena and characterization Safety Telecommunication computing Noise measurement Intelligent sensors

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Chronic Disease Prediction Model Using Integration of DBSCAN, SMOTE-ENN, and Random Forest

Chronic Disease Prediction Model Using Integration of DBSCAN...

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2022

作者： Fitriyani, Norma Latif Syafrudin, Muhammad Alfian, Ganjar Yang, Chuan-Kai Rhee, Jongtae Ulyah, Siti Maghfirotul Sejong University Department of Data Science Seoul Korea Republic of Vocational College Universitas Gadjah Mada Department of Electrical Engineering and Informatics Yogyakarta Indonesia National Taiwan University of Science and Technology Department of Information Management Taipei Taiwan Dongguk University Department of Industrial and Systems Engineering Seoul Korea Republic of Department of Mathematics Khalifa University Abu Dhabi United Arab Emirates Department of Mathematics Airlangga University Surabaya Indonesia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Heart disease (HD) is number one chronic disease and becomes a major cause of worldwide disability and death. Aside of HD, type 2 diabetes (T2D) is also as the most deathful diseases that causes serious issues if untreated and undetected. HD and T2D predictions are the most effective measures to control the HD and T2D. Thus, early HD and T2D predictions are important to help individuals in preventing the occurrence of the worst cases. This study proposes a chronic disease prediction model for HD and T2D prediction. The proposed study utilized random forest combined with DBSCAN as outlier detection method and SMOTE-ENN as data balancing method. Two HD datasets (Statlog and Cleveland) and one T2D dataset (NHIS Korea) were used for building the model and comparing the results with other existing machine learning (ML) algorithms, including GNB, LR, MLP, DT, and SVM. To measure the performance of the model, k-fold (10) cross-validation and several performance metrics including accuracy, precision, f-measure, and recall are applied in this study. The results show the model that we proposed outperforms other classification models, as well as previous studies, with accuracy rates 97.63%, 97.69%, and 94.85% for Statlog HD dataset, Cleveland HD dataset and NHIS T2D dataset, respectively. By utilizing the proposed model, it could increase the expectation in preventing the occurrence of the worst case and helping individuals in taking fast and precise actions when status of HD and T2D are detected. © 2022 IEEE.

关键词： Forecasting

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