Search results for: binding features

Authors: Jessemyn Modiini, Timothy Lynar, Elena Sitnikova

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We present a novel enhanced compartmental model for malware spread analysis in cyber security. This paper applies cyber security data features to epidemiological compartmental models to model the infectious potential of malware. Compartmental models are most efficient for calculating the infectious potential of a disease. In this paper, we discuss and profile epidemiologically relevant data features from a Domain Name System (DNS) dataset. We then apply these features to epidemiological compartmental models to network traffic features. This paper demonstrates how epidemiological principles can be applied to the novel analysis of key cybersecurity behaviours and trends and provides insight into threat modelling above that of kill-chain analysis. In applying deterministic compartmental models to a cyber security use case, the authors analyse the deficiencies and provide an enhanced stochastic model for cyber epidemiology. This enhanced compartmental model (SUEICRN model) is contrasted with the traditional SEIR model to demonstrate its efficacy.

Keywords: cybersecurity, epidemiology, cyber epidemiology, malware

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Michael Steve Lopez Bernabe

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Political news coverage of tabloid sheets as one of the print media molds or influences public opinions and perceptions. In this study, Critical Discourse Analysis was employed to 30 political news taken from major tabloid sheets in the Philippines in order to determine the linguistics features and other features characterizing the political news in tabloids such as discursive styles, news topics or contexts, journalistic roles and news sources. The political underpinnings through framing were also explored in the study. The results revealed that the linguistics features of the news coverage include moods and modalities (morphology), passivity and transitivity, nominalization, appositives and embedding (syntax), and pre-modifications, the use of verbs and omissions (grammatical features). The discursive features were direct or indirect speech; cohesion; endophora and classifications. In terms of news sources were politicians, experts, and journalists; and the tabloid perform the journalistic roles such as an intervention, watchdog, loyal-facilitator, service, infotainment and civic. The news was also evident of different political underpinnings such as game or strategic framing, conflict framing, human interest framing, attrition of responsibility framing, morality framing, economic consequences framing and issue framing.

Keywords: critical discourse analysis, political news, applied linguistics, Philippines, tabloid sheets

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Authors: Ghada E. Esheba, Ghadeer F. Alharthi, Duaa A. Alhejaili, Rawan E. Hudairy, Wafaa A. Altaezi, Raghad M. Alhejaili

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Background: Inflammatory bowel disease (IBD) consists of two specific gastrointestinal disorders: ulcerative colitis (UC) and Crohn's disease (CD). Despite their distinct natures, these two diseases share many similar etiologic, clinical and pathological features, as a result, their accurate differential diagnosis may sometimes be difficult. Correct diagnosis is important because surgical treatment and long-term prognosis differ from UC and CD. Aim: This study aims to study the characteristic clinicopathological features which help in the differential diagnosis between UC and CD, and assess the disease activity in ulcerative colitis. Materials and methods: This study was carried out on 50 selected cases. The cases included 27 cases of UC and 23 cases of CD. All the cases were examined using H& E and immunohistochemically for bcl-2 expression. Results: Characteristic features of UC include: decrease in mucous content, irregular or villous surface, crypt distortion, and cryptitis, whereas the main cardinal histopathological features seen in CD were: epitheloid granuloma, transmural chronic inflammation, absence of mucin depletion, irregular surface, or crypt distortion. 3 cases of UC were found to be associated with dysplasia. UC mucosa contains fewer Bcl-2+ cells compared with CD mucosa. Conclusion: This study using multiple parameters such clinicopathological features and Bcl-2 expression as studied by immunohistochemical stain, helped to gain an accurate differentiation between UC and CD. Furthermore, this work spotted the light on the activity and different grades of UC which could be important for the prediction of relapse.

Keywords: Crohn's disease, dysplasia, inflammatory bowel disease, ulcerative colitis

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies.

Keywords: nouns, features, network, category specificity

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Authors: Ayad Dalloo, Sulaf Dalloo

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Electrocardiogram (ECG) recording is prone to complications, on analysis by physicians, due to noise and artifacts, thus creating ambiguity leading to possible error of diagnosis. Such drawbacks may be overcome with the advent of high resolution Methods, such as Discrete Wavelet Analysis and Digital Signal Processing (DSP) techniques. This ECG signal analysis is implemented in three stages: ECG preprocessing, features extraction and classification with the aim of realizing high resolution ECG diagnosis and improved detection of abnormal conditions in the heart. The preprocessing stage involves removing spurious artifacts (noise), due to such factors as muscle contraction, motion, respiration, etc. ECG features are extracted by applying DSP and suggested sloping method techniques. These measured features represent peak amplitude values and intervals of P, Q, R, S, R’, and T waves on ECG, and other features such as ST elevation, QRS width, heart rate, electrical axis, QR and QT intervals. The classification is preformed using these extracted features and the criteria for cardiovascular diseases. The ECG diagnostic system is successfully applied to 12-lead ECG recordings for 12 cases. The system is provided with information to enable it diagnoses 15 different diseases. Physician’s and computer’s diagnoses are compared with 90% agreement, with respect to physician diagnosis, and the time taken for diagnosis is 2 seconds. All of these operations are programmed in Matlab environment.

Keywords: ECG diagnostic system, QRS detection, ECG baseline removal, cardiovascular diseases

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Authors: Virendra Nath, Vipin Kumar

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Background: TypeII Diabetes mellitus is a foremost health problem worldwide, predisposing to increased mortality and morbidity. Undesirable effects of the current medications have prompted the researcher to develop more potential drug(s) against the disease. The peroxisome proliferator-activated receptors (PPARs) are members of the nuclear receptors family and take part in a vital role in the regulation of metabolic equilibrium. They can induce or repress genes associated with adipogenesis, lipid, and glucose metabolism. Aims: Investigation of PPARα/γ agonistic hits were screened by hierarchical virtual screening followed by molecular dynamics simulation and knowledge-based structure-activity relation (SAR) analysis using approved PPAR α/γ dual agonist. Methods: The PPARα/γ agonistic activity of compounds was searched by using Maestro through structure-based virtual screening and molecular dynamics (MD) simulation application. Virtual screening of nuclear-receptor ligands was done, and the binding modes with protein-ligand interactions of newer entity(s) were investigated. Further, binding energy prediction, Stability studies using molecular dynamics (MD) simulation of PPARα and γ complex was performed with the most promising hit along with the structural comparative analysis of approved PPARα/γ agonists with screened hit was done for knowledge-based SAR. Results and Discussion: The silicone chip-based approach recognized the most capable nine hits and had better predictive binding energy as compared to the reference drug compound (Tesaglitazar). In this study, the key amino acid residues of binding pockets of both targets PPARα/γ were acknowledged as essential and were found to be associated in the key interactions with the most potential dual hit (ChemDiv-3269-0443). Stability studies using molecular dynamics (MD) simulation of PPARα and γ complex was performed with the most promising hit and found root mean square deviation (RMSD) stabile around 2Å and 2.1Å, respectively. Frequency distribution data also revealed that the key residues of both proteins showed maximum contacts with a potent hit during the MD simulation of 20 nanoseconds (ns). The knowledge-based SAR studies of PPARα/γ agonists were studied using 2D structures of approved drugs like aleglitazar, tesaglitazar, etc. for successful designing and synthesis of compounds PPARγ agonistic candidates with anti-hyperlipidimic potential.

Keywords: computational, diabetes, PPAR, simulation

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Authors: Sanpachai Huvanandana, Settapong Malisuwan, Soparwan Tongyuak, Prust Pannachet, Anong Phoepueak, Navneet Madan

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Many regions of Thailand are prone to flooding due to tropical climate. A commonly increasing precipitation in this continent results in risk of flooding. Many efforts have been implemented such as drainage control system, multiple dams, and irrigation canals. In order to decide where the drainages, dams, and canal should be appropriately located, the flooding risk area should be determined. This paper is aimed to identify the appropriate features that can be used to classify the flooding risk area in Thailand. Several features have been analyzed and used to classify the area. Non-supervised clustering techniques have been used and the results have been compared with ten years average actual flooding area.

Keywords: flood area clustering, geographical information system, flood features

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Authors: Raymon Maac, Michael Arthus Muega, Joyce Ann Calingasan, Elva Maureen Gorospe

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Critical pedagogy is known for its advocacy of humanistic and liberating education. Its far-reaching approach helps students to understand and analyze their own situations and the realities happening in their society. However, this pedagogy together with its promising features is not well-known in the Philippines. This paper determines the place of critical pedagogy in the new values education curriculum and analyzes its features in the K-12 Values Education curriculum and textbook. The study examines the position of critical pedagogy in the Philippine K-12 Values Education curriculum by closely studying and comparing their features; and scrutinizes the Grade 8 Values Education textbook specifically modules 4, 8, 10 and 13 which comprises 25% of the total 16 modules. The said modules are concerned with the role of the family in the preservation of social justice, which is one of the objectives of critical pedagogy. The findings in this research were based on the pieces of evidence gathered from the curriculum and textbook itself. Based on the evaluation done, the study found out that the ideas of critical pedagogy were the same with that of the objectives of K-12 Values Education Curriculum. Due to this, values education teachers can utilize critical pedagogy in their subject. In addition, the K-12 Values Education curriculum exhibits some of the features of critical pedagogy such as authentic student empowerment and critical thinking. Lastly, some features of critical pedagogy are also evident in some of the general parts and recommended activities in the K-12 Values Education textbook while other activities need to be fully developed by both teacher and students to reflect the genuine critical pedagogy.

Keywords: authentic student empowerment, critical pedagogy, critical thinking, liberating education

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Authors: Rita Darmayanti

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This study aims at investigating the discourse pattern and structure of research article abstracts. The characteristics of the language used in abstracts written by non-native English-speaking (NNES) novice researchers are mainly examined in terms of rhetorical moves and the degree of variability of the rhetorical features as indicated by the structure of clauses and the linguistic features of the text. To this end, 20 abstracts written by undergraduate students of the accounting department at the State Polytechnic of Malang in 2018-2019 were employed as the data of this study. Findings showed that the most frequently used pattern of the rhetorical move is I(Introduction)-P(Purpose)-M(Method)-Pr(Product or Result)-C(Conclusion) with the significant use of active sentence and present and past tense. The findings of the study are projected to be utilized for evaluating the quality of students’ abstracts and generating a pedagogical proposal of ESP writing course or at least providing a critical review of current practices in ESP program intended for non-native English students at tertiary level.

Keywords: rhetorical features, rhetorical moves, non-native English-speaking novice researchers, research abstract

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Authors: Alanod Alqasim, Yasmine Iskandarani, Sita Algethami, Jawaher alzughaiby

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This study focuses on the usage of AI (Artificial Intelligence) systems and features on the Instagram application and how it influences user experience and satisfaction. The aim is to evaluate the techniques and current capabilities, restrictions, and potential future directions of AI in an Instagram application. Following a concise explanation of the core concepts underlying AI usage on Instagram. To answer this question, 19 randomly selected users were asked to complete a 9-question survey on their experience and satisfaction with the app's features (Filters, user preferences, translation tool) and authenticity. The results revealed that there were three prevalent allegations. These declarations include that Instagram has an extremely attractive user interface; secondly, Instagram creates a strong sense of community; and lastly, Instagram has an important influence on mental health.

Keywords: AI (Artificial Intelligence), instagram, features, satisfaction, experience

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Authors: Dennis Okora Amima Ondieki

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Fertility transition has been identified to be affected by numerous factors. This research aimed to investigate the most real factors affecting fertility transition in Kenya. These factors were firstly extracted from the literature convened into demographic features, social, and economic features, social-cultural features, reproductive features and modernization features. All these factors had 23 factors identified for this study. The data for this study was from the Kenya Demographic and Health Surveys (KDHS) conducted in 1999-2003 and 2003-2008/9. The data was continuous, and it involved the mean birth order for the ten periods. Principal component analysis (PCA) was utilized using 23 factors. Principal component analysis conveyed religion, region, education and marital status as the real factors. PC scores were calculated for every point. The identified principal components were utilized as forecasters in the multiple regression model, with the fertility level as the response variable. The four components were found to be affecting fertility transition differently. It was found that fertility is affected positively by factors of region and marital and negatively by factors of religion and education. These four factors can be considered in the planning policy in Kenya and Africa at large.

Keywords: fertility transition, principal component analysis, Kenya demographic health survey, birth order

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

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Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

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Authors: Tatiana V. Raudina, Sergey P. Kulizhskiy

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A large part of Russia is located in permafrost areas. These areas are widely used because there are concentrated valuable natural resources. Therefore to explore of cryosols it is important due to the significant increase of anthropogenic stress as well as the problem of global climate change. In the north of Western Siberia permafrost phenomena is widespread. Permafrost as a factor of soil formation and cryogenesis as a process have a great impact on the soil formation of these areas. Based on the research results of permafrost-affected soils tundra landscapes formed in the central part of the Tazovskiy Peninsula in cryogenic conditions, data were obtained which characterize the morphological features of soils. The specificity of soil cover distribution and manifestation of soil-forming processes within the study area are noted. Permafrost features such as frost cracking, cryoturbation, thixotropy, movement of humus are formed. The formation of these features is increased with the development of the territory. As a consequence, there is a change in the components of the environment and the destruction of the soil cover.

Keywords: gleyed and nongleyed soils, permafrost, soil cryogenesis (pedocryogenesis), soil-forming macroprocesses

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Authors: Fausto Morais

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Artificial intelligence has been widely used in law. Programs are able to classify suits, to identify decision-making patterns, to predict outcomes, and to formalize legal arguments as well. In Brazil, the artificial intelligence victor has been classifying cases to supreme court’s standards. When those programs act doing those tasks, they simulate some kind of legal decision and legal arguments, raising doubts about how artificial intelligence can be integrated into legal reasoning. Taking this into account, the following three issues are identified; the problem of hypernormatization, the argument of legal anthropocentrism, and the artificial legal principles. Hypernormatization can be seen in the Brazilian legal context in the Supreme Court’s usage of the Victor program. This program generated efficiency and consistency. On the other hand, there is a feasible risk of over standardizing factual and normative legal features. Then legal clerks and programmers should work together to develop an adequate way to model legal language into computational code. If this is possible, intelligent programs may enact legal decisions in easy cases automatically cases, and, in this picture, the legal anthropocentrism argument takes place. Such an argument argues that just humans beings should enact legal decisions. This is so because human beings have a conscience, free will, and self unity. In spite of that, it is possible to argue against the anthropocentrism argument and to show how intelligent programs may work overcoming human beings' problems like misleading cognition, emotions, and lack of memory. In this way, intelligent machines could be able to pass legal decisions automatically by classification, as Victor in Brazil does, because they are binding by legal patterns and should not deviate from them. Notwithstanding, artificial intelligent programs can be helpful beyond easy cases. In hard cases, they are able to identify legal standards and legal arguments by using machine learning. For that, a dataset of legal decisions regarding a particular matter must be available, which is a reality in Brazilian Judiciary. Doing such procedure, artificial intelligent programs can support a human decision in hard cases, providing legal standards and arguments based on empirical evidence. Those legal features claim an argumentative weight in legal reasoning and should serve as references for judges when they must decide to maintain or overcome a legal standard.

Keywords: artificial intelligence, artificial legal principles, hypernormatization, legal anthropocentrism argument, legal reasoning

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Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

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Authors: Hassan Youssef Mohamed

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In this paper, we show new wave equations, and by using the equations, we concluded that the strong force and the weak force are not fundamental, but they are quantum effects for electromagnetism. This result is different from the current scientific understanding about strong and weak interactions at all. So, we introduce three evidences for our theory. First, we prove the asymptotic freedom phenomenon in the strong force by using our model. Second, we derive the nuclear shell model as an approximation of our model. Third, we prove that the leptons do not participate in the strong interactions, and we prove the short ranges of weak and strong interactions. So, our model is consistent with the current understanding of physics. Finally, we introduce the electron-positron model as the basic ingredients for protons, neutrons, and all matters, so we can study all particles interactions and nuclear interaction as many-body problems of electrons and positrons. Also, we prove the violation of parity conservation in weak interaction as evidence of our theory in the weak interaction. Also, we calculate the average of the binding energy per nucleon.

Keywords: new wave equations, the strong force, the grand unification theory, hydrogen atom, weak force, the nuclear shell model, the asymptotic freedom, electron-positron model, the violation of parity conservation, the binding energy

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Kinga Mylkie, Pawel Nowak, Marta Z. Borowska

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The most important proteins in human serum responsible for drug binding are human serum albumin (HSA) and α1-acid glycoprotein (AGP). The AGP molecule is a glycoconjugate containing a single polypeptide chain composed of 183 amino acids (the core of the protein), and five glycan branched chains (sugar part) covalently linked by an N-glycosidic bond with aspartyl residues (Asp(N) -15, -38, -54, -75, - 85) of polypeptide chain. This protein plays an important role in binding alkaline drugs, a large group of drugs used in psychiatry, some acid drugs (e.g., coumarin anticoagulants), and neutral drugs (steroid hormones). The main goal of the research was to obtain magnetic nanoparticles coated with biopolymers in a chemically modified form, which will have highly reactive functional groups able to effectively immobilize the glycoprotein (acid α1-glycoprotein) without losing the ability to bind active substances. The first phase of the project involved the chemical modification of biopolymer starch. Modification of starch was carried out by methods of organic synthesis, leading to the preparation of a polymer enriched on its surface with aldehyde groups, which in the next step was coupled with 3-aminophenylboronic acid. Magnetite nanoparticles coated with starch were prepared by in situ co-precipitation and then oxidized with a 1 M sodium periodate solution to form a dialdehyde starch coating. Afterward, the reaction between the magnetite nanoparticles coated with dialdehyde starch and 3-aminophenylboronic acid was carried out. The obtained materials consist of a magnetite core surrounded by a layer of modified polymer, which contains on its surface dihydroxyboryl groups of boronic acids which are capable of binding glycoproteins. Magnetic nanoparticles obtained as carriers for plasma protein immobilization were fully characterized by ATR-FTIR, TEM, SEM, and DLS. The glycoprotein was immobilized on the obtained nanoparticles. The amount of mobilized protein was determined by the Bradford method.

Keywords: glycoproteins, immobilization, magnetic nanoparticles, polysaccharides

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Lavinia Ruta, Ileana Farcasanu

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The yeast surface display (YSD) technique enables the expression of proteins on yeast cell surfaces, facilitating the identification and isolation of proteins with targeted binding properties, such as nanobodies. Nanobodies, derived from camelid species, are single-domain antibody fragments renowned for their high affinity and specificity towards target proteins, making them valuable in research and potentially in therapeutics. Their advantages include a compact size (~15 kDa), robust stability, and the ability to target challenging epitopes. The project endeavors to establish and validate a platform for producing Green Fluorescent Protein (GFP) and mCherry nanobodies using the yeast surface display method. mCherry, a prevalent red fluorescent protein sourced from coral species, is commonly utilized as a genetic marker in biological studies due to its vibrant red fluorescence. The GFP-nanobody, a single variable domain of heavy-chain antibodies (VHH), exhibits specific binding to GFP, offering a potent means for isolating and engineering fluorescent protein fusions across various biological research domains. Both GFP and mCherry nanobodies find specific utility in cellular imaging and protein analysis applications.

Keywords: YSD, nanobodies, GFP, Saccharomyces cerevisiae

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Authors: Mona Alharbi

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Melioidosis has emerged as a lethal disease. Unfortunately, the molecular mechanisms of virulence and pathogenicity of Burkholderia pseudomallei remain unknown. However, proteomics research has selected putative targets in B. pseudomallei that might play roles in the B. pseudomallei virulence. BPSL 2418 putative protein has been predicted as a free methionine sulfoxide reductase and interestingly there is a link between the level of the methionine sulfoxide in pathogen tissues and its virulence. Therefore in this work, we describe the cloning expression, purification, and crystallization of BPSL 2418 and the solution of its 3D structure using X-ray crystallography. Also, we aimed to identify the substrate binding and reduced forms of the enzyme to understand the role of BPSL 2418. The gene encoding BPSL2418 from B. pseudomallei was amplified by PCR and reclone in pETBlue-1 vector and transformed into E. coli Tuner DE3 pLacI. BPSL2418 was overexpressed using E. coli Tuner DE3 pLacI and induced by 300μM IPTG for 4h at 37°C. Then BPS2418 purified to better than 95% purity. The pure BPSL2418 was crystallized with PEG 4000 and PEG 6000 as precipitants in several conditions. Diffraction data were collected to 1.2Å resolution. The crystals belonged to space group P2 21 21 with unit-cell parameters a = 42.24Å, b = 53.48Å, c = 60.54Å, α=γ=β= 90Å. The BPSL2418 binding MES was solved by molecular replacement with the known structure 3ksf using PHASER program. The structure is composed of six antiparallel β-strands and four α-helices and two loops. BPSL2418 shows high homology with the GAF domain fRMsrs enzymes which suggest that BPSL2418 might act as methionine sulfoxide reductase. The amino acids alignment between the fRmsrs including BPSL 2418 shows that the three cysteines that thought to catalyze the reduction are fully conserved. BPSL 2418 contains the three conserved cysteines (Cys⁷⁵, Cys⁸⁵ and Cys¹⁰⁹). The active site contains the six antiparallel β-strands and two loops where the disulfide bond formed between Cys⁷⁵ and Cys¹⁰⁹. X-ray structure of free methionine sulfoxide binding and native forms of BPSL2418 were solved to increase the understanding of the BPSL2418 catalytic mechanism.

Keywords: X-Ray Crystallography, BPSL2418, Burkholderia pseudomallei, Melioidosis

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

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Authors: Saurabh B. Ganorkar, Atul A. Shirkhedkar

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The need for investigations protecting against toxic impurities though seems to be a primary requirement; the impurities which may prove non - toxic can be explored for their therapeutic potential if any to assist advanced drug discovery. The essential role of pharmaceutical analysis can thus be extended effectively to achieve it. The present study successfully achieved these objectives with characterization of major degradation products as impurities for Zileuton which has been used for to treat asthma since years. The forced degradation studies were performed to identify the potential degradation products using Ultra-fine Liquid-chromatography. Liquid-chromatography-Mass spectrometry (Time of Flight) and Proton Nuclear Magnetic Resonance Studies were utilized effectively to characterize the drug along with five major oxidative and hydrolytic degradation products (DP’s). The mass fragments were identified for Zileuton and path for the degradation was investigated. The characterized DP’s were subjected to In-Silico studies as XP Molecular Docking to compare the gain or loss in binding affinity with 5-Lipooxygenase enzyme. One of the impurity of was found to have the binding affinity more than the drug itself indicating for its potential to be more bioactive as better Antiasthmatic. The close structural resemblance has the ability to potentiate or reduce bioactivity and or toxicity. The chances of being active biologically at other sites cannot be denied and the same is achieved to some extent by predictions for probability of being active with Prediction of Activity Spectrum for Substances (PASS) The impurities found to be bio-active as Antineoplastic, Antiallergic, and inhibitors of Complement Factor D. The toxicological abilities as Ames-Mutagenicity, Carcinogenicity, Developmental Toxicity and Skin Irritancy were evaluated using Toxicity Prediction by Komputer Assisted Technology (TOPKAT). Two of the impurities were found to be non-toxic as compared to original drug Zileuton. As the drugs are purposed and repurposed effectively the impurities can also be; as they can have more binding affinity; less toxicity and better ability to be bio-active at other biological targets.

Keywords: UFLC, LC-MS-TOF, NMR, Zileuton, impurities, toxicity, bio-activity

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Authors: Tanushree Jaitly, Shailendra Gupta, Leila Taher, Gerold Schuler, Julio Vera

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Genomics-based personalized medicine is a promising approach to fight aggressive tumors based on patient's specific tumor mutation and expression profiles. A remarkable case is, dendritic cell-based immunotherapy, in which tumor epitopes targeting patient's specific mutations are used to design a vaccine that helps in stimulating cytotoxic T cell mediated anticancer immunity. Here we present a computational pipeline for epitope-based personalized cancer vaccines using patient-specific haplotype and cancer mutation profiles. In the workflow proposed, we analyze Whole Exome Sequencing and RNA Sequencing patient data to detect patient-specific mutations and their expression level. Epitopes including the tumor mutations are computationally predicted using patient's haplotype and filtered based on their expression level, binding affinity, and immunogenicity. We calculate binding energy for each filtered major histocompatibility complex (MHC)-peptide complex using docking studies, and use this feature to select good epitope candidates further.

Keywords: cancer immunotherapy, epitope prediction, NGS data, personalized medicine

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Authors: Hamid Hossein Khezri, Afsaneh Javdani-Mallak

Abstract:

Fast inhibitory neurotransmission in the mammalian nervous system is largely mediated by GABAA receptors, chloride-selective members of the superfamily of pentameric Cys-loop receptors. Cannabidiol (CBD) is one of the members of cannabinoid compounds found in cannabis. CBD and Cannabinol (CBN), as the other extract of plant Cannabis were able to reduce myofascial pain in rats with immunosuppressive and anti-inflammatory activities. In this study, we accomplished protein-protein BLAST, and the sequence was found to be for Gamma-aminobutyric acid receptor subunit alpha-1 (GBRA1) chain A and its 3D structure was subsequently downloaded from Protein Data Bank. The structures of the ligands, cannabinol, and cannabidiol, were obtained from PubChem. After the necessary process of the obtained files, AutoDock Vina was used to perform molecular docking. Docking between the ligands and GBRA1 chain A revealed that cannabinol has a higher affinity to GBRA1 (binding energy = -7.5 kcal/mol) compared to cannabidiol (binding energy = -6.5 kcal/mol). Furthermore, cannabinol seems to be able to interact with 10 residues of the protein, out of which 3 are in the neurotransmitter-gated ion-channel transmembrane domain of GBRA1, whereas cannabidiol interacts with two other residues. Although the results of this project do not indicate the activating /or inhibitory capability of the studied compounds, it suggests that cannabinol can act as a relatively strong ligand for GBRA1.

Keywords: protein-ligand docking, cannabinol, cannabidiol, GBRA1

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