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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Carter, John Mancoridis, Spiros Protopapas, Pavlos Dept. of Computer Science Drexel University United States Institute for Applied Computational Science Harvard University United States

ISBN: (纸本)9780998133188

As malware becomes increasingly stealthy and more difficult to detect, behavioral malware detection has become the preferred method of detection, which uses representative run-time data from the device to determine if an infection has occurred. In this work, we collected kernel-level system calls from a router serving IoT devices during periods of benign behavior and periods of known malware infection. The system calls were processed using our custom-trained sys2vec model, which created contextual embeddings for each system call observed. We then subjected the data to a classifier using a Gated Recurrent Unit (GRU) with an Attention layer. Although this pipeline performed well for noisy, easy-to-detect malware, it struggled with stealthier malware. To combat this, we trained a classifier that uses a custom-trained BERT encoder in place of the GRU/Attention layers, which results in much better detection at a usable false positive rate (FPR) ≤ 1 × 10-5,. © 2025 IEEE computer Society. All rights reserved.

关键词： computer viruses

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Optimizing Potato Disease Detection in Pakistan with Machine Learning: A Comparative Analysis 3

Optimizing Potato Disease Detection in Pakistan with Machine...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Irfan Khalil, Manahal Ghulam, Safoora Mahmood, Jabar Batool, Khadija Khalid, Nibtal Arshad, Pakiza Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (纸本)9798331504861

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity. ©2024 IEEE.

关键词： Random forests

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LAN: Learning to Adapt Noise for Image Denoising

LAN: Learning to Adapt Noise for Image Denoising

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Changjin Kim Tae Hyun Kim Sungyong Baik Dept. of Computer Science Hanyang University Dept. of Data Science Hanyang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image Denoising with the emergence of advanced deep learning architectures and real-world datasets, recent denoising net-works struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a Denoising network to new noise distribution. Instead, in this work, we shift our focus to adapting the input noise itself, rather than adapting a network. Thus, we keep a pretrained network frozen, and adapt an input noise to capture the fine-grained deviations. As such, we propose a new denoising algorithm, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently, the proposed framework exhibits performance improvement on images with unseen noise, displaying the potential of the proposed research direction.

关键词： Training Adaptation models computer vision Noise Noise reduction Deep architecture Pattern recognition

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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Deep Reinforcement Learning Approach for Prioritized Heterogeneous Object Tracking in UAV Swarms

Deep Reinforcement Learning Approach for Prioritized Heterog...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Nithya, B. Subash, Neethu Vignesh, Hari National Institute of Technology Dept. of Computer Science and Engg Tiruchirappalli India St.Joseph's College Department of Data Science Tiruchirappalli India

ISBN: (纸本)9798350314946

Deep Reinforcement learning(DRL) has recently showcased its boundless potential in Multi-agent collaboration. There exist DRL-based models that strive to unravel the Unmanned aerial vehicle (UAV) Multi-target tracking predicaments, however, the conundrum of tracing desired targets persists as most research endeavors solely focus on the agents' policies while neglecting the dynamic environment, which harbors elusive and unpredictable targets. The proposed model executes Prioritized Heterogeneous Object Tracking (PHOT) using DRL, to furnish a solution for preferred Multi-target tracking by upholding the policy of UAV swarms. Tracking preferred targets assumes paramount importance as these targets necessitate meticulous monitoring within the ever-changing environmental backdrop, particularly during surveillance missions employing UAV swarms, which can harness the full potential of the UAV swarms in real-time. A YOLO v4-based algorithm is put forth, which can trail prioritized targets in a tracking by detection paradigm. To maintain the policy of UAV swarms, a dueling double deep Q-learning algorithm is deployed, complemented by a reward function that stimulates UAVs to track the preferred targets instantaneously. The UAVDT dataset is employed for the simulations, and the results evince that PHOT exhibits robustness, suitable for deployment in a real-time environment, amassing a highly commendable average cumulative reward over time, and enabling actions predicated on unidentical prioritized targets. © 2023 IEEE.

关键词： Reinforcement learning

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Next Generation Intelligent Traffic Signal Control: Empowering Electronics Consumers with Edge-AIoT Capabilities

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1926-1934页

作者： Chavhan, Suresh Doswada, Rohit Gupta, Deepak Gunjal, Saymam Rodrigues, Joel J.P.C. School of Data Sciences Indian Institute of Science Education and Research Kerala Thiruvananthapuram India C-DoT Karnataka Bangalore India Dept. of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Centre for Research Impact and Outcome Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering University of Visvesvaraya College of Engineering Karnataka Bangalore India Federal University of Piaui PI Teresina Brazil

Traffic congestion has become a major issue that is being faced by the majority of road users. The increasing vehicle usage, and the lack of space and funds to construct new transport infrastructure, further complicates the issue. In this scenario, it is important to come up with an intelligent and economical solution that improves the quality of road users' service. The problem with the traffic handling framework is signal timings are fixed which is not adaptive to the density of vehicles. To address this issue we propose an Edge-Augument Artificial Intelligence of Things (AIoT) road user cooperation for traffic management. The proposed system efficiently utilizes electronic devices to learn and adapt to changing traffic conditions in real-time. By optimizing the traffic signal timings based on the actual traffic conditions, adaptive systems reduce delay, improve traffic flow, reduce fuel consumption and pollution, and improve the electronics consumers' and road users' experiences. The proposed system has been tested with real-time experiments by integrating Electronic devices like cameras, smartphones, and AGX Xavier (edge device) with Cloud (ThingSpeak). The proposed system is verified by simulating the proposed system in the SUMO traffic simulator and its reliability is concluded by comparing the waiting time, depart delay, running, and halt time with the existing traditional method. © 2025 IEEE.

关键词： Traffic congestion

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Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness 4

Efficient MILP Decomposition in Quantum Computing for ReLU N...

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Franco, Nicola Wollschläger, Tom Poggel, Benedikt Günnemann, Stephan Lorenz, Jeanette Miriam Fraunhofer Institute for Cognitive Systems Iks Munich Germany Technical Univ. of Munich Dept. of Computer Science Munich Data Science Institute Germany

ISBN: (纸本)9798350343236

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90% of qubits compared to existing methods on quantum annealing and gate-based quantum computers. © 2023 IEEE.

关键词： Qubits

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Football Prediction System using Gaussian Naïve Bayes Algorithm 2

Football Prediction System using Gaussian Naïve Bayes Algor...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Athish, V.P. Rajeswari, D. Nandha, S.S. Sree Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350346640

Soccer (or, more colloquially, football) is among the most popular sports around the globe, with a thriving economy valued at more than $400 billion and billions of supporters (estimated) worldwide. Predicting match results has always piqued people's interest, and studying game results has shown to be extremely beneficial for corporate success and player development. There are many machine learning approaches for game prediction, however, it is believed the Bayesian approach could be very helpful in this scenario (given reliable historical data). The proposed model has been enforced on authentic squad information including match results collected from *** and other websites like ***. Observations indicate that the Gaussian Naive Bayes Approach is capable of predicting the results of a football match with an accuracy of 85.43%, which is a bit higher than the 79.81% accuracy that is achieved using the Decision Tree Classifier © 2023 IEEE.

关键词： Big data

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Generating High-Quality Distributed Representations of Words Using Wiktionary and Sentence-BERT 16

Generating High-Quality Distributed Representations of Words...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Nishiura, Ryota Tsuchiya, Seiji Watabe, Hirokazu Graduate School of Science and Engineering Doshisha University Dept. of Information and Computer Science Kyotanabe Kyoto Japan Doshisha University Faculty of Science and Engineering Dept. of Intelligent Information Engineering and Science Kyotanabe Kyoto Japan

ISBN: (纸本)9798350377903

We propose an improved method to generate high-quality distributed representations of words. In our previous paper, we introduced a novel method to obtain distributed representations of words with BERT using Wiktionary as semantic sources of words. The previous method outperformed a Wrd2Vec model in a word similarity task, while there still had to be some room to improve in term of semantic accuracy. In this paper, our new method introduces one of Sentence-BERT models to text-embed the semantic sources instead of the original BERT model. We demonstrate that our proposed method outperforms both our previous method and the Word2Vec model in the tasks. © 2024 IEEE.

关键词： Embeddings

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