Research on IoT security situation awareness method based on evidence theory by Jian LI, Tinglu DONG, Jie LI

“…The security problem of IoT became more and more serious with its rapid development.Considering that the current IoT security situation awareness system lacks generality and excessively relies on expert knowledge, a IoT security situation awareness method based on improved D-S evidence theory was proposed in this paper.Fuzzy Gaussian membership function was used to calculate the vulnerability information membership matrix, which was normalized as evidence distribution matrix.The improved Topsis method was used to measure the evidence credibility.In order to fully restrain the credibility of conflicting evidence and improve the credibility of mutually supporting evidence, local credibility between two evidence was aggregated and the expected positive and negative solution vectors were improved according to the situation assessment scenario.And the weighted average method was used for vulnerability information fusion, to obtain the result of situational assessment.The result of situational awareness was fused with the time discount and high-risk vulnerability information discount evidence theory.At the same time, the IoT vulnerability information at different moments was considered comprehensively, the evidence was adaptively and dynamically weighted with the ratio information of high-risk vulnerability.The experimental results show that in the fusion of different numbers of evidence bodies and four common conflicting evidence, the improved Topsis method has higher fusion probability on credible proposition.In the aspect of situation assessment, the risk degree of current system is accurately assessed.And in the aspect of situational awareness, this discount evidence theory can predict the probability of high risk and critical risk, which is more effective than the traditional D-S evidence theory.According to this theory, a IoT security situational awareness method process was proposed, which would be used to guide engineering practice.In the future, the relationship between vulnerabilities can be considered and richer information between vulnerabilities can be extracted for vulnerability exploiting, so that the result of situation assessment is more accurate and reasonable.On the other hand, for situational awareness, game theory can be adopted in the process of dynamic game between the attacker and defender.…”
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Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining by Smitha Bidadi Anjan Prasad, Raja Praveen Kumar Nakka

“…The vectorized data are then processed using various machine learning algorithms to classify the polarity of tweets. …”
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Automatic Compressive Sensing of Shack–Hartmann Sensors Based on the Vision Transformer by Qingyang Zhang, Heng Zuo, Xiangqun Cui, Xiangyan Yuan, Tianzhu Hu

“…This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. …”
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Distribution of <i>Solidago canadensis</i> L. (Asteraceae) in the Russian Federation by N. N. Luneva, E. N. Mysnik

“…The map was built using IDRISI Selva 17.0 and vectorized in MapInfo 16.0. S. canadensis has spread quite widely in Russia, growing in the European part, the North Caucasus, the Urals, Western Siberia, and the Far East. …”
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MLAR-Net: A Multilevel Attention-Based ResNet Module for the Automated Recognition of Emotions Using Single-Channel EEG Signals by M. Maithri, U. Raghavendra, Anjan Gudigar, Samir Kumar Praharaj, Karthikeyan Sriram, Massimo Salvi, Chai Hong Yeong, Filippo Molinari, U. Rajendra Acharya

“…Our study identifies channel number 24 (T7) as the most effective for emotion classification, achieving an average accuracy of 98.06% using a cubic support vector machine and a maximum accuracy of 99.51% using fine K-Nearest Neighbors. …”
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Monitoring and Assessment of Slope Hazards Susceptibility Around Sarez Lake in the Pamir by Integrating Small Baseline Subset InSAR with an Improved SVM Algorithm by Yang Yu, Changming Zhu, Majid Gulayozov, Junli Li, Bingqian Chen, Qian Shen, Hao Zhou, Wen Xiao, Jafar Niyazov, Aminjon Gulakhmadov

“…These deformation measurements were combined with key environmental factors to construct a susceptibility evaluation model based on the Information Value and Support Vector Machine (IV-SVM) methods. The results revealed a distinct spatial deformation pattern, characterized by greater activity in the western region than in the east. …”
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The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes by Chaoling Wu, Wanyi Liu, Pengfei Mei, Yunyun Liu, Jian Cai, Lu Liu, Juan Wang, Xuefeng Ling, Mingxue Wang, Yuanyuan Cheng, Manbi He, Qin He, Qi He, Xiaoliang Yuan, Jianlin Tong

“…While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. …”
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Older people and stroke: a machine learning approach to personalize the rehabilitation of gait by Elvira Maranesi, Federico Barbarossa, Leonardo Biscetti, Marco Benadduci, Elisa Casoni, Ilaria Barboni, Fabrizia Lattanzio, Lorenzo Fantechi, Daniela Fornarelli, Enrico Paci, Sara Mecozzi, Manuela Sallei, Mirko Giannoni, Giuseppe Pelliccioni, Giovanni Renato Riccardi, Valentina Di Donna, Roberta Bevilacqua

“…This underscores the basal ganglia’s role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.…”
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Regression-based leaf nitrogen concentration estimation of young Cephalotaxus hainanensis in small and imbalanced samples by Mengmeng Shi, Tian Wang, Ling Lin, Qingxue Li, Zhulin Chen, Xingjing Chen, Peng Wang, Xuefeng Wang

“…The study also utilized advanced metrics (F1-score, recall, and precision for regression) for evaluating regression models to compare and assess the accuracy of support vector regression (SVR) and gradient-boosted trees (XGBoost) combined with different pretreatments, particularly emphasizing prediction accuracy in rare cases. …”
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High-Precision Phenotyping in Soybeans: Applying Multispectral Variables Acquired at Different Phenological Stages by Celí Santana Silva, Dthenifer Cordeiro Santana, Fábio Henrique Rojo Baio, Ana Carina da Silva Cândido Seron, Rita de Cássia Félix Alvarez, Larissa Pereira Ribeiro Teodoro, Carlos Antônio da Silva Junior, Paulo Eduardo Teodoro

“…Logistic regression (RL) and support vector machine (SVM) models showed better performance in the early reproductive stage R1, with accuracies above 55 for CC, close to 0.1 for Kappa, and close to 0.4 for the F-score. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…Proper management of digestible energy (DE) is crucial for maintaining pig health, promoting growth, and facilitating reproduction by supporting essential biological processes. Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and multilayer perceptron (MLP) were applied across four datasets, with the input parameters including body weight of pigs (BW), inside temperature (IT), inside relative humidity (IRH), and inside CO2 concentration (ICO2) of pig barns. …”
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In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes by A. Lampert, R. Hankers, T. Feuerle, T. Rausch, M. Cremer, M. Angermann, M. Bitter, J. Füllgraf, H. Schulz, U. Bestmann, K. B. Bärfuss

“…The instrumentation of both aircraft consisted of a nose boom with sensors for measuring the wind vector, temperature and humidity and, additionally, a surface temperature sensor. …”
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THE INFLUENCE OF HUMAN LEUKEMIA DIFFERENTIATION FACTOR ON ABILITY OF BLOOD CELLS TO PRODUCE IL-1β, IL-1ra AND IL-8 IN CHRONIC ATROPHIC GASTRITIS, ADENOMAS AND ADENOCARCINOMAS OF TH... by A. V. Sosnina, E. S. Mikhailova, D. V. Morozov, A. I. Autenshlyus, N. A. Varaksin, L. A. Shpagina, E. E. Vorobyeva, T. V. Rakitina, V. M. Lipkin

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Integrated Assessment of the Quality of Functioning of Local Electric Energy Systems by Waldemar Wójcik, Petro Lezhniuk, Cezary Kaczmarek, Viacheslav Komar, Iryna Hunko, Nataliia Sobchuk, Laura Yesmakhanova, Zhazira Shermantayeva

“…The integral indicator of the functioning of complex systems is based on a combination of the theory of Markov processes and the criterion method of similarity theory. …”
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Harnessing Multiscale Topographic Environmental Variables for Regional Coral Species Distribution Models by Annie S. Guillaume, Renata Ferrari, Oliver Selmoni, Véronique J. L. Mocellin, Hugo Denis, Melissa Naugle, Emily Howells, Line K. Bay, Stéphane Joost

“…For this, we obtained and processed three distinct bathymetric digital depth models that we treat as DEMs, which are available across the GBR extent: (i) Allen Coral Atlas (ACA) at 10 m, (ii) DeepReef at 30 m and (iii) DeepReef at 100 m. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

“…Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. …”
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Surface-based morphometry of the cerebral cortex in cognitive impairments of varying severity in patients with age-related cerebral small vessel disease by Elena I. Kremneva, Larisa A. Dobrynina, Kamila V. Shamtieva, Victoria V. Trubitsyna, Zukhra S. Gadzhieva, Angelina G. Makarova, Maria M. Tsypushtanova, Marina V. Krotenkova

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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…IntroductionThe liver is one of the vital organs of human body that performs some of the most crucial biological processes such as protein and biochemical synthesis, which is required for digestion and cleansing. …”
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TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines by Sadam Hussain, Usman Naseem, Mansoor Ali, Daly Betzabeth Avendaño Avalos, Servando Cardona-Huerta, Beatriz Alejandra Bosques Palomo, Jose Gerardo Tamez-Peña

“…Abstract Background Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. …”
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Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types by Mahrukh Zakir, Alishbah Saddiqa, Mawara Sheikh, Lalarukh Zakir, Fatima Sami, Faisal Sardar Ahmad, Sadaf Abdul Rauf, Iqra Ali, Zahid Muneer, Wadi B. Alonazi, Abdul Rauf Siddiqi

“…RNA Seq datasets consisting of 49 HER2 + and 44 TNBC breast tumor samples were retrieved and pre-processed. Differentially Expressed Genes (DEGs) along with their logFC and p-values were fetched. …”
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