Information security vulnerability scoring model for intelligent vehicles by Haiyang YU, Xiuzhen CHEN, Jin MA, Zhihong ZHOU, Shuning HOU

“…More and more electronic devices are integrated into the modern vehicles with the development of intelligent vehicles.There are various design flaws and vulnerabilities hidden in a large number of hardware, firmware and software.Therefore, the vulnerabilities of intelligent vehicles have become the most important factor affecting the vehicle safety.The safety of vehicles is seriously affected by the disclosure of a large number of vulnerabilities, and the wide application of smart cars is also restricted.Vulnerability management is an effective method to reduce the risk of vulnerabilities and improve vehicle security.And vulnerability scoring is one the important step in vulnerability management procedure.However, current method have no capability assessing automotive vulnerabilities reasonably.In order to handle this problem, a vulnerability scoring model for intelligent vehicles was proposed, which was based on CVSS.The attack vector and attack complexity were optimized, and property security, privacy security, functional safety and life safety were added to characterize the possible impact of the vulnerabilities according to the characteristics of intelligent vehicles.With the machine learning method, the parameters in CVSS scoring formula were optimized to describe the characteristics of intelligent vehicle vulnerabilities and adapt to the adjusted and new added weights.It is found in case study and statistics that the diversity and distribution of the model are better than CVSS, which means the model can better score different vulnerabilities.And then AHP is used to evaluate the vulnerability of the whole vehicle based on the vulnerability score of the model, a score is given representing the risk level of whole vehicle.The proposed model can be used to evaluate the severity of information security vulnerabilities in intelligent vehicles and assess the security risks of the entire vehicle or part of the system reasonably, which can provide an evidence for fixing the vulnerabilities or reinforcing the entire vehicle.…”
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Evaluating Urban Community Sustainability by Integrating Housing, Ecosystem Services, and Landscape Configuration by Liang Zhou, Haowei Mu, Bao Wang, Bo Yuan, Xuewei Dang

“…In this study, we perform a sustainable-oriented land use scheme using multisource remote sensing, machine learning, and object-based postclassification refinement. …”
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Water quality assessment for aquaculture using deep neural network by Rajeshwarrao Arabelli, T. Bernatin, Venkataramana Veeramsetty

“…The proposed model is validated by comparing with other machine learning models like support vector machine, K-Nearest Neighbour and Naive Bayes classifier in terms of metrics like accuracy, f1-score, precision and recall. …”
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Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals by Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun-Woo Shin, Hyung-Sin Kim

“…Abstract Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the ‘black-box’ nature. …”
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Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture by H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan

“…DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. …”
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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by wafiq azizah, soffiana agustin

“…The proposed methodology integrates two feature extraction techniques, Histogram of Oriented Gradients (HOG) and Texture Moments, with the Random Forest machine learning algorithm. The research process encompasses four key stages: pre-processing, feature extraction, classification, and system evaluation, where the accuracy of individual and combined feature extraction methods is analyzed. …”
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CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions by Zishuo Zeng, Jin Guo, Jiao Jin, Xiaozhou Luo

“…However, conventional machine learning approaches encounter challenges due to data scarcity and class imbalance. …”
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AI Methods for Antimicrobial Peptides: Progress and Challenges by Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, Cesar de laFuente‐Nunez

“…However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. …”
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Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

“…In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. …”
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Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers. by Andrew Erickson, Sandy Figiel, Timothy Rajakumar, Srinivasa Rao, Wencheng Yin, Dimitrios Doultsinos, Anette Magnussen, Reema Singh, Ninu Poulose, Richard J Bryant, Olivier Cussenot, Freddie C Hamdy, Dan Woodcock, Ian G Mills, Alastair D Lamb

“…Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. …”
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A CNN-LSTM-Based Model to Forecast Stock Prices by Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang

“…At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. …”
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Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks by Shougi S. Abosuliman, Saleem Abdullah, Nawab Ali

“…Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. …”
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3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection by Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, Daniel Pomarède

“…It comprises different tools based on data augmentation and machine learning, which proved to be very efficient on simulated data and increase the quality of the experimental data analysis. …”
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EdgeSecureDP: Strengthening IoHTs Differential Privacy Through Graphvariate Skellam by Mohamed Amjath, Shagufta Henna

“…Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring privacy and security in healthcare applications. …”
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Predicting wind power using LSTM, Transformer, and other techniques by Arun Kumar M, Rithick Joshua K, Sahana Rajesh, Caroline Dorathy Esther J, Kavitha Devi MK

“…In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. …”
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A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing by Massimo Pacella, Antonio Papa, Gabriele Papadia, Emiliano Fedeli

“…Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. …”
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Prediction OPEC oil price utilizing long short-term memory and multi-layer perceptron models by Hiyam Abdulrahim, Safiya Mukhtar Alshibani, Omer Ibrahim, Azhari A. Elhag

“…These results signify a notable progress in the use of machine learning techniques for predicting OPEC oil prices. …”
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Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers by Bikash Chandra Saha, Joshuva Arockia Dhanraj, M. Sujatha, R. Vallikannu, Mohana Alanazi, Ahmad Almadhor, Ravishankar Sathyamurthy, Kuma Gowwomsa Erko, V. Sugumaran

“…This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. …”
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Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province by Huisheng Yu, Ge Song, Tong Li, Yanjun Liu

“…Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…Despite the proliferation of machine learning and deep learning algorithms for fall detection, their efficacy remains hampered by resilience, robustness, and adaptability challenges across varied input scenarios. …”
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