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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Asif Iqubal Happy Happy Subodh Kant Tiwari USICT Guru Gobind Singh Indraprastha University New Delhi India Dept. of Computer Science & Engg. The NorthCap University Gurugram Haryana India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

the rapid increase in the number of Android devices has made it a major target for malware attacks that put many users around the globe at risk of security breaches. As technology advances and malware becomes more complex and diverse at a pace conventional methods relying solely on recognizing specific patterns are becoming less effective in safeguarding against these threats. this study delves into the use of Machine Learning (ML) models as a line of defense against Android Malware. We delve into advancements in ML techniques specifically tailored for detecting Android malware, highlighting their advantages and constraints. We are looking into how different models such as Logistic Regression (LR) Support Vector Machine (SVM) Decision Trees (DT) k. Nearest Neighbor (k. NN) Naive Bayes (NB) Neural Networks (NN) Random Forest (RF) Gradient Boosting Machine (GBM) AdaBoost (AdaB) Bagging Classifier (BC) and XGBoost (XGB) can accurately identify malicious applications with high precision and low rates of false positives using the " TUNADROMD " dataset. the models were thoroughly evaluated based on accuracy, precision, recall, F1 score, and ROC AUC criteria. the outcomes reveal that methods such as k-NN, NN, and RF achieved a flawless score of 1 across all measures signifying their exceptional accuracy in recognizing malware without any incorrect identifications. the results highlight the promise of ML in transforming Android security and laying groundwork for creating more dynamic defenses against malware threats.

关键词： Support vector machines Logistic regression Accuracy Artificial neural networks Feature extraction Malware pattern recognition Security Random forests Classification tree analysis

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Enhanced Accessibility for Recognizing Indian and American Sign Language through Deep Learning Techniques

Enhanced Accessibility for Recognizing Indian and American S...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： P. Jayadharshini Lalitha Krishnasamy S. Santhiya Harsine K U Geetha P Logu B Department of Artificial Intelligence Kongu Engineering College Erode Tamil Nadu Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Sign language detection, specifically for Indian Sign Language (ISL) and American Sign Language (ASL), plays a crucial role in bridging communication gaps between hearing impaired and normal people..this paper proposes a systematic methodology using different deep learning models to detect ISL and ASL from an image dataset of sign language gestures. the algorithms probed were 2D Convolutional Neural Networks (CNN), ResNet-50, Long Short Term Memory (LSTM) and VGG16. By using a dataset we conducted an extensive comparative analysis of these algorithms. the results portrayed LSTM as the best predictive classifier with an amazing 97.97% accuracy. this excellent result makes LSTM a strong model for the classification of sign languages with high accuracy. the target classes involved several signs from ISL and ASL are thus extensive for recognizing and interpreting both these languages by using a number of algorithms we make the predictive model more robust as it can handle multiple kinds of data patterns corresponding to different sign gestures. the outcomes of our investigation present the very first step towards an intelligent sign language detection system. A well-recognized and fast-emerging potential of deep learning is in the automatic detection of sign languages, which introduces a drastic positive change in communication efficiency as this research field grows exponentially. this could greatly improve the ability of a person to interact and the accessibility of an individual to sign languages.

关键词： Deep learning Sign language Accuracy Predictive models Assistive technologies Prediction algorithms Classification algorithms pattern recognition Convolutional neural networks Long short term memory

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Predicting protein stability changes from sequences using support vector machines

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bioinformatics 2005年第Sup2期21卷 54-58页

作者： Capriotti, E Fariselli, P Calabrese, R Casadio, R Univ Bologna Dept Biol CIRB Lab Biocomp Bologna Italy

Motivation: the prediction of protein stability change upon mutations is key to understanding protein folding and misfolding. At present, methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are, however, necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases. Results: We develop a method based on support vector machines that, starting from the protein sequence, predicts the sign and the value of free energy stability change upon single point mutation. We show that the accuracy of our predictor is as high as 77% in the specific task of predicting the Delta Delta G sign related to the corresponding protein stability. When predicting the Delta Delta G values, a satisfactory correlation agreement with the experimental data is also found. As a final blind benchmark, the predictor is applied to proteins with a set of disease-related SNPs, for which thermodynamic data are also known. We found that our predictions corroborate the view that disease-related mutations correspond to a decrease in protein stability. Availability: http://***/cgi/predictors/I-Mutant2.0/***.

关键词： Algorithms* Amino Acid Sequence Amino Acid Substitution Artificial Intelligence* Computer Simulation Models, Chemical* Molecular Sequence Data pattern recognition, Automated/methods* Protein Denaturation Protein Denaturation Protein Folding Proteins/chemistry* Sequence Alignment/methods Sequence Analysis, Protein/methods* Structure-Activity Relationship

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Preface

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2002年 2396卷 V页

作者： Caelli, Terry Amin, Adnan Duin, Bob Kamel, Mohamed de Ridder, Dick University of Alberta Dept. of Computing Science Athabasca Hall EdmontonAB Canada University of New SouthWales School of Computer Science and Engineering SydneyNSW Australia Delft University of Technology Dept. of Applied Physics Pattern Recognition Group Lorentzweg 1 Delft Netherlands University ofWaterloo Dept. of Systems Design Engineering McGill University WaterlooON Canada

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Analysis of Timing pattern of Speech as Possible Indicator for Near-Term Suicidal Risk and Depression in Male Patients

Analysis of Timing Pattern of Speech as Possible Indicator f...

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2012 4th international conference on Signal Processing Systems(ICSPS 2012)

作者： Nik Wahidah Hashim Mitch Wilkes Ronald Salomon Jared Meggs Department of Electrical Engineering Vanderbilt University Department of Psychiatry Vanderbilt University School of Medicine

Patients who are diagnosed with depression without appropriate clinical recognition of their hidden suicidal tendencies are at elevated risk of making a suicide attempts. An important clinical problem remains the differentiation between non-suicidal and more lethal episodes of depression. In an effort to find a reliable method that could assist clinicians in risk assessment, information in the speech signal has been found to contain characteristic changes associated with high risk suicidal states. Among numerous characteristics available, this paper addresses the question of which speech signal contribute most to the discrimination between high risk suicidal and depressed patients. Analysis is based on features related to the timing patterns of speech (voiced, unvoiced and silence), specifically the Transition Parameters and Interval Probability Density Functions (PDF). Automatic speech data sets were collected from readings of a standard "rainbow passage" essay. Linear and quadratic classifiers were used to obtain the decision boundary for the pairwise classification and the classifier performance was estimated using the method of Equal-Test-Train data, K-cross validation and Jackknife. Use of the jackknife procedure as a means to measure a classifier performance for all-data classification, within the first data set, revealed a single Transition Parameter to be a significant discriminator with 74% correct classification. Certain combinations of interval pdf for voiced and silence yielded 75%-100% correct classification. Results achieved 83% correct classification for a single Transition Parameter and 94% overall correct classification with 100% high risk prediction when using a single Transition Parameter combined with a single bin from voiced or silence interval pdf.

关键词： classification, suicide, depression, speech pauses, prediction, voiced

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Rapid Large Noise Denoising for Scan Lines in Range Images

Rapid Large Noise Denoising for Scan Lines in Range Images

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2011 4th IEEE international conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Jian WANG a,b Student Member,IEEE a.State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai, 200240,P.R.China b.Cyber Physical System R&D Center,the third Research Institute of Ministry of Public Security, Shanghai,201204,P.R.China Lin MEI Cyber Physical System R&D Center,the third Research Institute of Ministry of Public Security, Shanghai 201204,P.R.China Zhen-qiang YAO State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai, 200240,P.R.China Yuan YAO Information Center,Nanjing University of Aeronautics and Astronautics,Nanjing,Jiangsu,210016,P.R.China Yang ZHANG Clolrectal Department,Nanjing Hospital of T.C.M, Nanjing,Jiangsu,210001,P.R.China

A novel rapid large noise denoising method for range image is *** a 2D scan line acquired by a laser range finder,distribution features of large noises(LNs)are analyzed,and then a mathematical representation of the features is provided by defining a few new coefficients. Subsequently,a rule-based distinguishing criterion is formulated to detect two types of LNs:dropouts and invaders. the traditional mean filter is improved by an automotive non- LN neighbor searching procedure.A compositive algorithm with a very low computational complexity has been implemented as an embedded module in our self-developed software with *** experiment on real range image is performed,and the results indicate that the proposed method can detect all the large noises accurately and denoise them with a significant *** is proven that the method is suitable for practical applications on industrial or other fields.

关键词： range image denoising large noises detectetion and removal computer vision pattern recognition laser radar scanning

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2021年 12964 LNCS卷 ix-x页

作者： Haq, Nandinee Johnson, Patricia Maier, Andreas Würfl, Tobias Yoo, Jaejun Sunnybrook Health Science Centre TorontoON Canada NYU Grossman School of Medicine New York CityNY United States Pattern Recognition Lab at FAU Erlangen Germany Siemens Healthineers Erlangen Germany Ulsan National Institute of Science and Technology Ulsan Korea Republic of

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A fully Bayesian model to cluster gene-expression profiles

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bioinformatics 2005年第Sup2期21卷 130-136页

作者： Vogl, C Sanchez-Cabo, F Stocker, G Hubbard, S Wolkenhauer, O Trajanoski, Z Vet Med Univ Vienna Inst Genet & Anim Breeding A-1210 Vienna Austria Graz Univ Technol Inst Genom & Bioinformat A-8010 Graz Austria Graz Univ Technol Christian Doppler Lab Genom & Bioinformat A-8010 Graz Austria Univ Manchester Fac Life Sci Manchester M60 1QD Lancs England Univ Rostock Inst Informat D-18051 Rostock Germany

Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance;results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data. Results: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. the number of classes does not need to be specified in advance;instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing. Availability: the code is available at http://***/BayesianClustering/.

关键词： Algorithms* Artificial Intelligence Bayes theorem Cluster Analysis* Computer Simulation Gene Expression Profiling/methods* Models, Genetic* Multigene Family/physiology* Oligonucleotide Array Sequence Analysis/methods* pattern recognition, Automated/methods*

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Public microarray repository semantic annotation with ontologies employing text mining and expression profile correlation

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BMC bioinformatics 2008年第s10期9卷 O5-O5页

作者： Ruau, David Kolarik, Corinna Mevissen, Heinz-theodor Mueller, Emmanuel Assent, Ira Krieger, Ralph Seidl, thomas Hofmann-Apitius, Martin Zenke, Martin Rhein Westfal TH Aachen Univ Med Sch Inst Biomed Engn Dept Cell Biol D-52074 Aachen Germany Fraunhofer Inst SCAI D-53754 St Augustin Germany Bonn Aachen Int Ctr Informat Technol Dept Appl Life Sci Informat D-53113 Bonn Germany Rhein Westfal TH Aachen Data Management & Data Explorat Grp Aachen Germany Rhein Westfal TH Aachen Helmholtz Inst Biomed Engn D-52074 Aachen Germany

Public microarray repository annotation Gene Expression Omnibus (GEO) is the largest public web repository of microarray experiments. GEO, like ArrayExpress and Stanford MicroArray Database, provides descriptions of microarray experiments in free text making it difficult to search and comprehensively link those data to other knowledge resources. Text mining techniques applied to microarray experiment annotation are challenged by poor and/or ambiguous free text description and consequently leave some objects unlabelled. Previous work organized GEO entries at the level of series (GSE) and data sets (GDS) using the Unified Medical Language System (UMLS). GSE and GDS description are often too broad and a better quality of annotation can be achieved if the GEO samples (GSM) are considered directly. Here we report on a novel approach for annotating GSM objects by employing a combination of text mining and global gene expression similarity. We hypothesize that the biological material analyzed on microarrays is related if unlabeled and labeled objects are highly similar in expression values and hence the class/annotation of one object can help annotate an unlabeled object. Our new method allows us to achieve a higher percentage of semantic annotation by combining both types of information stored in microarray databases. Results the GSM free text description (downloaded from GEO in November 2007) was mined using ProMiner, a software for Named Entity recognition based on dictionaries of cell, tissue and disease ontologies from OBO plus cell line resources. this resulted in 73.5-97.6% class labeling of the GSM objects (Table 1). Next the labeled objects were used to annotate the unlabeled objects. We computed the correlation matrix for all the objects where the raw data were available and followed the nearest neighbor approach to identify the nearest labeled object within a δ range. the δ value is an input parameter determined empirically and limits the propagation of too diss

关键词： Microarray Experiment Gene Expression Omnibus Text Mining Unify Medical Language System Semantic Annotation

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Growing Bayesian network models of gene networks from seed genes

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bioinformatics 2005年第Sup2期21卷 224-229页

作者： Peña, JM Björkegren, J Tegnér, J Linkoping Univ Dept Phys & Measurement Technol S-58183 Linkoping Sweden Karolinska Inst Ctr Genom & Bioinformat S-17177 Stockholm Sweden

Motivation: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. this means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. this is not a trivial decision. We propose an alternative approach to overcome this problem. Results: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes;find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.

关键词： Algorithms Artificial Intelligence* Bayes theorem Computer Simulation Gene Expression/physiology* Gene Expression Profiling/methods* Models, Genetic* Neural Networks (Computer) pattern recognition, Automated/methods Proteome/metabolism* Signal Transduction/genetics* Signal Transduction/physiology*

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