Search results for: DNA sequences classification

Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Amir Shahab Shahabi, Mohsen Hasirian

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Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies.

Keywords: credit card fraud, deep learning, attention mechanism, recurrent neural networks

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

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Authors: Yasser F. Hassan

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Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

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Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Vyacheslav Furtak, Merja Roivainen, Olga Mirochnichenko, Majid Laassri, Bella Bidzhieva, Tatiana Zagorodnyaya, Vladimir Chizhikov, Konstantin Chumakov

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Molecular epidemiology and environmental surveillance are parts of a rational strategy to control infectious diseases. They have been widely used in the worldwide campaign to eradicate poliomyelitis, which otherwise would be complicated by the inability to rapidly respond to outbreaks and determine sources of the infection. The conventional scheme involves isolation of viruses from patients and the environment, followed by their identification by nucleotide sequences analysis to determine phylogenetic relationships. This is a tedious and time-consuming process that yields definitive results when it may be too late to implement countermeasures. Because of the difficulty of high-throughput full-genome sequencing, most such studies are conducted by sequencing only capsid genes or their parts. Therefore the important information about the contribution of other parts of the genome and inter- and intra-species recombination to viral evolution is not captured. Here we propose a new approach based on the rapid concentration of sewage samples with tangential flow filtration followed by deep sequencing and reconstruction of nucleotide sequences of viruses present in the samples. The entire nucleic acids content of each sample is sequenced, thus preserving in digital format the complete spectrum of viruses. A set of rapid algorithms was developed to separate deep sequence reads into discrete populations corresponding to each virus and assemble them into full-length consensus contigs, as well as to generate a complete profile of sequence heterogeneities in each of them. This provides an effective approach to study molecular epidemiology and evolution of natural viral populations.

Keywords: poliovirus, eradication, environmental surveillance, laboratory diagnosis

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Masoud Soltanialvar, Ali Bagherpour

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Documented cases of human infection with H9N2 avian influenza viruses, first detected in 1999 in Hong Kong and China, indicate that these viruses can be directly transmitted from birds to humans. In this study, we characterized the mutation in the Hemagglutinin (HA) genes and proteins that correlates with a shift in affinity of the Hemagglutinin (HA) protein from the “avian” type sialic receptors to the “human” type in 10 Iranian isolates. We delineated the genomes and receptor binding profile of HA gene of some field isolates and established their phylogenetic relationship to the other Asian H9N2 sub lineages. A total of 1200 tissue samples collected from 40 farms located in various states of Iran during 2008 – 2010 as part of a program to monitor Avian Influenza Viruses (AIV) infection. To determine the genetic relationship of Iranian viruses, the Hemagglutinin (HA) genes from ten isolates were amplified and sequenced (by RT-PCR method). Nucleotide sequences (orf) of the (HA) genes were used for phylogenetic tree construction. Deduced amino acid sequences showed the presence of L226 (234 in H9 numbering) in all ten Iranian isolates which indicates a preference to binding of α (2–6) sialic acid receptors, so these Iranian H9N2 viruses have the potential to infect human beings. These isolates showed high degree of homology with 2 human H9N2 isolates A/HK/1073/99, A/HK/1074/99. Phylogenetic analysis of showed that all the HA genes of the Iranian H9N2 viruses fall into a single group within a G1-like sublineage which had contributed as donor of six internal genes to H5N1 highly pathogenic avian influenza. The results of this study indicated that all Iranian viruses have the potential to emerge as highly pathogenic influenza virus, and considering the homology of these isolates with human H9N2 strains, it seems that the potential of these avian influenza isolates to infect human should not be overlooked.

Keywords: influenza virus, hemagglutinin, neuraminidase, Iran

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Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

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Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Alexander A. Stakheev, Larisa V. Samokhvalova, Sergey K. Zavriev

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Fusarium fungi are among the most devastating plant pathogens distributed all over the world. Significant reduction of grain yield and quality caused by Fusarium leads to multi-billion dollar annual losses to the world agricultural production. These organisms can also cause infections in immunocompromised persons and produce the wide range of mycotoxins, such as trichothecenes, fumonisins, and zearalenone, which are hazardous to human and animal health. Identification of Fusarium fungi based on the morphology of spores and spore-forming structures, colony color and appearance on specific culture media is often very complicated due to the high similarity of these features for closely related species. Modern Fusarium taxonomy increasingly uses data of crossing experiments (biological species concept) and genetic polymorphism analysis (phylogenetic species concept). A number of novel Fusarium sibling species has been established using DNA barcoding techniques. Species recognition is best made with the combined phylogeny of intron-rich protein coding genes and ribosomal DNA sequences. However, the internal transcribed spacer of (ITS), which is considered to be universal DNA barcode for Fungi, is not suitable for genus Fusarium, because of its insufficient variability between closely related species and the presence of non-orthologous copies in the genome. Nowadays, the translation elongation factor 1 alpha (TEF1α) gene is the “gold standard” of Fusarium taxonomy, but the search for novel informative markers is still needed. In this study, we used two novel DNA markers, frataxin (FXN) and heat shock protein 90 (HSP90) to discover phylogenetic relationships between Fusarium species. Multilocus phylogenetic analysis based on partial sequences of TEF1α, FXN, HSP90, as well as intergenic spacer of ribosomal DNA (IGS), beta-tubulin (β-TUB) and phosphate permease (PHO) genes has been conducted for 120 isolates of 19 Fusarium species from different climatic zones of Russia and neighboring countries using maximum likelihood (ML) and maximum parsimony (MP) algorithms. Our analyses revealed that FXN and HSP90 genes could be considered as informative phylogenetic markers, suitable for evolutionary and taxonomic studies of Fusarium genus. It has been shown that PHO gene possesses more variable (22 %) and parsimony informative (19 %) characters than other markers, including TEF1α (12 % and 9 %, correspondingly) when used for elucidating phylogenetic relationships between F. avenaceum and its closest relatives – F. tricinctum, F. acuminatum, F. torulosum. Application of novel DNA barcodes confirmed the fact that F. arthrosporioides do not represent a separate species but only a subspecies of F. avenaceum. Phylogeny based on partial PHO and FXN sequences revealed the presence of separate cluster of four F. avenaceum strains which were closer to F. torulosum than to major F. avenaceum clade. The strain F-846 from Moldova, morphologically identified as F. poae, formed a separate lineage in all the constructed dendrograms, and could potentially be considered as a separate species, but more information is needed to confirm this conclusion. Variable sites in PHO sequences were used for the first-time development of specific qPCR-based diagnostic assays for F. acuminatum and F. torulosum. This work was supported by Russian Foundation for Basic Research (grant № 15-29-02527).

Keywords: DNA barcode, fusarium, identification, phylogenetics, taxonomy

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

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In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

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Authors: AbdelRahman A. Faragalla, Mohamed H. Alqhtani, Mohamed M. M.Ahmed

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Saudi Arabia has currently been beset by a barrage of bizarre assemblages of subterranean termite fauna, inflicting heavy catastrophic havocs on human valued properties in various homes, storage facilities, warehouses, agricultural and horticultural crops including okra, sweet pepper, tomatoes, sorghum, date palm trees, citruses and many forest domains and green lush desert oases. The most pressing urgent priority is to use modern technologies to alleviate the painstaking obstacle of taxonomic identification of these injurious noxious pests that might lead to effective pest control in both infested agricultural commodities and field crops. Our study has indicated the use of DNA fingerprinting technologies, in order to generate basic information of the genetic similarity between 3 predominant families containing the most destructive termite species. The methodologies included extraction and DNA isolation from members of the major families and the use of randomly selected primers and PCR amplifications with the nucleotide sequences. GC content and annealing temperatures for all primers, PCR amplifications and agarose gel electrophoresis were also conducted in addition to the scoring and analysis of Random Amplification Polymorphic DNA-PCR (RAPDs). A phylogenetic analysis for different species using statistical computer program on the basis of RAPD-DNA results, represented as a dendrogram based on the average of band sharing ratio between different species. Our study aims to shed more light on this intriguing subject, which may lead to an expedited display of the kinship and relatedness of species in an ambitious undertaking to arrive at correct taxonomic classification of termite species, discover sibling species, so that a logistic rational pest management strategy could be delineated.

Keywords: DNA fingerprinting, Western Saudi Arabia, DNA primers, RAPD

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Authors: Bangun F., Crevits B., Bellet T., Banet A., Boy G. A., Katili I.

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This paper seeks the incoherencies in cognitive process during an accident of Powered Two Wheelers (PTW) by understanding the factual sequences of events and causal relations for each case of accident. The principle of this approach is undertaking in-depth investigations on case per case of PTW accidents based on elaborate data acquisitions on accident sites that officially stamped in Police Crash Report (PCRs) 2012 of Medan with criteria, involved at least one PTW and resulted in serious injury and fatalities. The analysis takes into account four modules: accident chronologies, perpetrator, and victims, injury surveillance, vehicles and road infrastructures, comprising of traffic facilities, road geometry, road alignments and weather. The proposal for improvement could have provided a favorable influence on the chain of functional processes and events leading to collision. Decision Aiding Processes (DAP) assists in structuring different entities at different decisional levels, as each of these entities has its own objectives and constraints. The entities (A) are classified into 6 groups of accidents: solo PTW accidents; PTW vs. PTW; PTW vs. pedestrian; PTW vs. motor-trishaw; and PTW vs. other vehicles and consecutive crashes. The entities are also distinguished into 4 decisional levels: level of road users and street systems; operational level (crash-attended police officers or CAPO and road engineers), tactical level (Regional Traffic Police, Department of Transportation, and Department of Public Work), and strategic level (Traffic Police Headquarters (TCPHI)), parliament, Ministry of Transportation and Ministry of Public Work). These classifications will lead to conceptualization of Problem Situations (P) and Problem Formulations (I) in DAP context. The DAP concerns the sequences process of the incidents until the time the accident occurs, which can be modelled in terms of five activities of procedural rationality: identification on initial human features (IHF), investigation on proponents attributes (PrAT), on Injury Surveillance (IS), on the interaction between IHF and PrAt and IS (intercorrelation), then unravel the sequences of incidents; filtering and disclosure, which include: what needs to activate, modify or change or remove, what is new and what is priority. These can relate to the activation or modification or new establishment of law. The PrAt encompasses the problems of environmental, road infrastructure, road and traffic facilities, and road geometry. The evaluation model (MP) is generated to bridge P and I since MP is produced by the intercorrelations among IHF, PrAT and IS extracted from the PCRs 2012 of Medan. There are 7 findings of incoherences: lack of knowledge and awareness on the traffic regulations and the risks of accidents, especially when riding between 0 < x < 10 km from house, riding between 22 p.m.–05.30 a.m.; lack of engagements on procurement of IHF Data by CAPO; lack of competency of CAPO on data procurement in accident-sites; no intercorrelation among IHF and PrAt and IS in the database systems of PCRs; lack of maintenance and supervision on the availabilities and the capacities of traffic facilities and road infrastructure; instrumental bias with wash-back impacts towards the TCPHI; technical robustness with wash-back impacts towards the CAPO and TCPHI.

Keywords: decision aiding processes, evaluation model, PTW accidents, police crash reports

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Authors: Fereshteh M. Haskouei, Habib Alimohammadian

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Magnetic susceptibility (χ) is operational to identify of late quaternary glacial-interglacial cycles in loess-paleosol sequences. It is well accepted that many loess-paleosol sequences bear witness to cold-dry/warm-humid periods, well known as glacial-interglacial cycles, respectively. For this study, loess-paleosol sequence of north-east of Iran was magnetically investigated. The study area is situated at about 8 km away of Neka city, on the main road of Sari-Behshahr, in Mazandaran Province, north of Iran. The youngest deposits of study area are the late Quaternary wind-blown accumulations. In this study, the total number of 117 samples was collected from loess-paleosols units. After that, the natural remnant magnetization (NRM) and magnetic susceptibility (MS) of the samples were measured. Variation of MS of more than 110 loess samples was plotted to reveal the correlation of the MS and paleoclimatic changes. This study aims reconstruction of climatic changes (glacial-interglacial and stadials-interstadials cycles). To confirm our results we compared MS (χ) and the curves of other investigations in paleoclimatology. This correspondence abled us to recognize worldly events in the study area such as: Younger Dryas, the Last Glacial Maximum (LGM), deglaciation of Northern Hemisphere etc. The obtained magnetic data indicate that during almost 50 ka, at least two glacial-interglacial periods occurred in north-east of Iran. Further, variation of χ values revealed short period of climatically cycles known as stadials-interstadials. We recognized 4 stadials and a single stadial as colder sub-periods for S₀ (recently soil-paleosol) and S₂ (lower paleosol), respectively, Moreover, we recognized 6 warmer sub-periods (interstadials) for L₁ (upper loess) and one interstadial L₂ (lower loess).

Keywords: glacial-interglacial cycles, Iran, last glacial maximum (LGM), loess, magnetic susceptibility (χ), Neka, stadials-interstadials sub-periods, younger dryas

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Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

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In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

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Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Sekena H. Abd El-Aziem, Heba A. M. Abd El-Kader, Sally S. Alam, Othman E. Othman

Abstract:

Growth hormone (GH) is an anabolic hormone synthesized and secreted by the somatotroph cells of the anterior lobe of the pituitary gland in a circadian and pulsatile manner, the pattern of which plays an important role in postnatal longitudinal growth and development, tissue growth, lactation, reproduction as well as protein, lipid and carbohydrate metabolism. The aim of this study was to detect the genetic polymorphism of GH gene in five camel breeds reared in Egypt; Sudany, Somali, Mowaled, Maghrabi and Falahy, using PCR-RFLP technique. Also this work aimed to identify the single nucleotide polymorphism between different genotypes detected in these camel breeds. The amplified fragment of camel GH at 613-bp was digested with the restriction enzyme MspI and the result revealed the presence of three different genotypes; CC, CT and TT in tested breeds and significant differences were recorded in the genotype frequencies between these camel breeds. The result showed that the Maghrabi breed that is classified as a dual purpose camels had higher frequency for allele C (0.75) than those in the other tested four breeds. The sequence analysis declared the presence of a SNP (C→T) at position 264 in the amplified fragment which is responsible for the destruction of the restriction site C^CGG and consequently the appearance of two different alleles C and T. The nucleotide sequences of camel GH alleles T and C were submitted to nucleotide sequences database NCBI/Bankit/GenBank and have accession numbers: KP143517 and KP143518, respectively. It is concluded that only one SNP C→T was detected in GH gene among the five tested camel breeds reared in Egypt and this nucleotide substitution can be used as a marker for the genetic biodiversity between camel breeds reared in Egypt. Also, due to the possible association between allele C and higher growth rate, we can used it in MAS for camels and enter the camels possess this allele in breeding program as a way for enhancement of growth trait in camel breeds reared in Egypt.

Keywords: camel breeds in Egypt, GH, PCR-RFLP, SNPs

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

Abstract:

Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

Abstract:

Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 103

Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

Abstract:

Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

Procedia PDF Downloads 174