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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Kunku, Kavitha Zaman, A.N.K. Roy, Kaushik Dept. of Physics & Computer Science WaterlooON Canada North Carolina a&t State University Dept. of Computer Science GreensboroNC United States

ISBN: (纸本)9781665430654

Vicious assaults, malware, and various ransomware pose a cybersecurity threat, causing considerable damage to computer structures, servers, and mobile and web apps across various industries and businesses. These safety concerns are important and must be addressed immediately. Ransomware detection and classification are critical for guaranteeing rapid reaction and prevention. This study uses the XGBoost classifier and Random Forest (RF) algorithms to detect and classify ransomware attacks. This approach involves analyzing the behaviour of ransom ware and extracting relevant features that can help distinguish between different ransomware families. The models are evaluated on a dataset of ransom ware attacks and demonstrate their effectiveness in accurately detecting and classifying ransomware. The results show that the XGBoost classifier, Random Forest Classifiers, can effectively detect and classify different ransomware attacks with high accuracy, thereby providing a valuable tool for enhancing cybersecurity. © 2023 IEEE.

关键词： Classification Cybersecurity Malware Malware Detection Random Forest Ransomware XGBoost

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sysBERT: Improved Behavioral Malware Detection using BERT Trained on sys2vec Embeddings 58

sysBERT: Improved Behavioral Malware Detection using BERT Tr...

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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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An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network 21

An Effective Deep Learning Approach for the Estimation of Pr...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Al-Mahdawi, Manar Kareem, Aythem Khairi Nafea, Ahmed Adil Shaban, Ali Muwafaq Aliesawi, Salah A. S. Al-Ani, Mohammed M College of Science AL-Nahrain University Baghdad Dept. Solid State Physics Iraq Ministry of Education Dept. of Heet Education General Anbar Heet31007 Iraq College of Computer Science and IT University of Anbar Ramadi Dept. Artificail Inteligence Iraq College of Computer Science and IT University of Anbar Ramadi Dept. Information System Iraq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Computer Science Iraq Selangor Bangi Malaysia

ISBN: (纸本)9798350374131

The prediction of proton energy shows a key part in various scientific and technological studies including particle physics, medical imaging, and radiation therapy. In the last years the regression techniques study a lot trying to discover the difficult relationships among proton properties energy, needing to be studying of additional advanced techniques. This study proposed Artificial Neural Networks (ANN) as a tool for predicting types of proton energy (Max, Total and A vg). This paper proposed a comprehensive methodology for developing an ANN model for proton energy predection, covering data preparation, model architecture design, evaluation, and prediction. This proposed can ability to learn difficult models and non-linear relationships from large datasets, making them well-suited for this task. The ANN proposed achieved the highest R2 on the Total of proton energy predation of 0.96 % while the best mean_squared_error on 0.03668 for testing dataset. The results displayed the efficiency and accuracy of the proposed ANN model in predicted proton energy. The findings highlight the ability of ANNs as a tool for proton energy prediction. © 2024 IEEE.

关键词： Forecasting

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 Dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Saha, Utsha Ahamed, Imtiaj Uddin Imran, Md Ashique Ahamed, Imam Uddin Hossain, Al-Amin Gupta, Ucchash Das Dept. of Computer Science North Dakota State University FargoND United States Agricultural Data Analytics Unit North Dakota State University FargoND United States Dept. of Computer Science University of South Dakota VermillionSD United States Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350350548

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv8lcls model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Development of Anomaly Detection with Variable Contexts on Refrigeration Data 28

Development of Anomaly Detection with Variable Contexts on R...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Meyer, Melina Gergeleit, Martin Krechel, Dirk RheinMain University of Applied Sciences Dept. of Design Computer Science Media Wiesbaden Germany

ISBN: (纸本)9798350339918

Anomaly detection in time series data is a well-known research topic and of great interest to the industry. However, industrial systems are complex due to the nature of real world plants and their interrelationships in the overall system. Industrial plants often cannot be considered in isolation because their environment affects them. Thus, industrial plants have variable contexts in which they are embedded. The combination of a variable context with the information of the plant poses a challenge in modeling AI approaches for anomaly *** the field of refrigeration, machine learning approaches are still in their early stages. In live systems, simple threshold values are often still used. There are only a few published approaches to detect error situations using machine learning on such *** this work, the challenges to such a model architecture are analyzed and a dataset of real refrigeration plants is built. Then, models for detection and classification are explored and their applicability to this type of data is evaluated. Furthermore, models are combined to add context to improve the results compared to models without context information. © 2023 IEEE.

关键词： Anomaly detection

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: applied Mathematics and physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Evaluation of the Effectiveness of Vulnerable Road User Clustering in C-V2X Systems

Evaluation of the Effectiveness of Vulnerable Road User Clus...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Rupp, Matthias Wischhof, Lars Munich University of Applied Sciences HM Dept. of Computer Science and Mathematics Munich Germany

ISBN: (纸本)9798350346473

Vulnerable Road Users (VRUs) are at special risk in road traffic. The dissemination of information about VRUs in Intelligent Transportation Systems (ITS) can help to protect VRUs from various risks. The Vulnerable Road User Awareness Message (VAM) format has been specified by ETSI as a way of distributing VRU-related information. The standard also describes a clustering mechanism that can help to reduce the number of generated messages and therefore prevent computing overload and network congestion in certain situations. This paper presents initial results on the effectiveness of the clustering approach proposed in the ETSI standard as well as its impact on position accuracy. Therefore, the clustering schemes are implemented in coupled network-and mobility simulations, modeling traffic situations with pedestrians and vehicles. In addition, the impact of one of the most relevant parameters for forming clusters, the maxClusterVelocityDifference, on clustering is assessed. The results indicate that VRU clustering can signifi-cantly reduce the number of VAM messages but also leads to a larger position error between the actual-and received position of individual VRUs. © 2023 IEEE.

关键词： Roads and streets

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Analysis of Housing Market to Predict Home Price

Analysis of Housing Market to Predict Home Price

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Ali, Md Liakat Magrini, David Cangelosi, Michael Ribeiro, David Rider University Dept. of Computer Science & Physics LawrencevilleNJ United States

ISBN: (纸本)9798350322972

Predicting the house market and home prices is still a challenging task. The price of a house depends on several factors and it is important to find the key factors that affect the price of a house in a particular location. In this study, we conducted an experiment to observe how certain variables affect housing prices. Principal component analysis (PCA) and a correlation heatmap were developed to document which variables had the highest correlation with price. At first, these variables were isolated for further development. Using the scikit-learn and XGBoost libraries for Python several models were developed for predicting housing prices within the area of Kings County, California, United States. From these models it was determined that using the full range of variables, rather than the most highly correlated variables, yielded the most accurate results. Out of the three regression models developed, the XGBoost model yielded the most accurate predictions compared to linear regression and random forest. © 2023 IEEE.

关键词： Multiple linear regression

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DEEP LEARNING TO SUPPORT 3D MAPPING CAPABILITIES OF A PORTABLE VSLAM-BASED SYSTEM 12

DEEP LEARNING TO SUPPORT 3D MAPPING CAPABILITIES OF A PORTAB...

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12th International Symposium on Mobile Mapping Technology, MMT 2023

作者： Padkan, N. Battisti, R. Menna, F. Remondino, F. Trento Italy Dept. Mathematics Computer Science and Physics University of Udine Italy

The use of vision-based localization and mapping techniques, such as visual odometry and SLAM, has become increasingly prevalent in the field of Geomatics, particularly in mobile mapping systems. These methods provide real-time estimation of the 3D scene as well as sensor's position and orientation using images or LiDAR sensors mounted on a moving platform. While visual odometry primarily focuses on the camera's position, SLAM also creates a 3D reconstruction of the environment. Conventional (geometric) and learning-based approaches are used in visual SLAM, with deep learning networks being integrated to perform semantic segmentation, object detection and dept. prediction. The goal of this work is to report ongoing developments to extend the GuPho stereo-vision SLAM-based system with deep learning networks for tasks such as crack detection, obstacle detection and dept. estimation. Our findings show how a neural network can be coupled to SLAM sequences in order to support 3D mapping application with semantic information. © Author(s) 2023.

关键词： Stereo vision

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