Search results for: artificial intelligence and genetic algorithms

Authors: Loo Fung Ying, Loo Fung Chiat

Abstract:

The formation of a musical identity that shapes the nation in this multiracial country reveals many complexities, conundrums, and contingencies. Creativity and identity formation at the level of an individual or a collective group further diversified musical expression, representation, and style, which has led to an absence of regularities. In addition, ‘contemporizing accretion,’ borrowing a term used by Schnelle in theology (2009), further complicates musical identity, authenticity, conception, and realization. Thus, in this paper, we attempt to define the issues surrounding the teaching and learning of the multiracial Malaysian national music identity. We also discuss unnecessary power hierarchies, interracial conflicts, and sentiments in the construct of a multiracial national music identity by referring to genetic origins, the evolution of music, and the neglected issues of representation and reception at a global level from a diachronic perspective. Lastly, by synthesizing Ladson-Billings, Gay, Kruger, and West-Burns’s culturally relevant/responsive pedagogical theories, we discuss possible analytic tools for consideration that are more multiculturally relevant and responsive for the teaching, learning, and construction of a multiracial Malaysian national music identity.

Keywords: Malaysia, music, multiracial, national music identity, culturally relevant/responsive pedagogy

Procedia PDF Downloads 201

Authors: Khaoula Azraiel, Mohamed Mehdi Abassi, Amel Sadraoui, Walid Hammami, Azouz Msaddek, Imed Cheikh, Maria Mancebo, Elisabet Perez-Navarro, Antonio Caruz, Henda Triki, Ahlem Djebbi

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IL28B rs12979860 genotype is confirmed as an important predictor of response to peginterferon/ribavirin therapy in patients with chronic hepatitis C (CHC). IFNL4 ss469415590 is a newly discovered polymorphism that could also affect the sustained virological response (SVR). The aim of this study was to evaluate the association of IL28B and IFNL4 genotypes with peginterferon/ribavirin treatment response in Tunisians patients with CHC and to determine which of these SNPs, was the stronger marker. A total of 120 patients were genotyped for both rs12979860 and ss469415590 polymorphisms. The association of each genetic marker with SVR was analyzed and comparison between the two SNPs was calculated by logistic regression models. For rs12979860, 69.6% of patients with CC, 41.8% with CT and 42.8% with TT achieved SVR (p = 0.003). Regarding ss469415590, 70.4% of patients with TT/TT genotype achieved SVR compared to 42.8% with TT/ΔG and 37.5% with ΔG /ΔG (p = 0.002). The presence of CC and TT/TT genotypes was independently associated with treatment response with an OR of 3.86 for each. In conclusion, both IL28B rs12979860 and IFNL4 ss469415590 variants were associated with response to pegIFN/RBV in Tunisian patients, without any additional benefit in performance for IFNL4. Our results are different from those detected in Sub-Saharan Africa countries.

Keywords: Hepatitis C virus, IFNL4, IL28B, Peginterferon/ribavirin, polymorphism

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Authors: Alexander Dao, Oscar Wambuguh

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Irritable Bowel Syndrome (IBS) is a heterogeneous functional bowel disease that is characterized by chronic visceral abdominal pain and abnormal bowel function and habits. Its multifactorial pathophysiology and mechanisms are still largely a mystery to the contemporary biomedical community, although there are many hypotheses to try to explain IBS’s presumed physiological, psychosocial, genetic, and environmental etiologies. IBS’s symptomatic presentation is varied and divided into four major subtypes: IBS-C, IBS-D, IBS-M, and IBS-U. Given its diverse presentation and unclear mechanisms, diagnosis is done through a combination of positive identification utilizing the “Rome IV Irritable Bowel Syndrome Criteria'' (Rome IV) diagnostic criteria while also excluding other potential conditions with similar symptoms. Treatment of IBS is focused on the management of symptoms using an assortment of pharmaceuticals, lifestyle changes, and dietary changes, with future potential in microbial treatment and psychotherapy as other therapy methods. Its chronic, heterogeneous nature and disruptive gastrointestinal (GI) symptoms are negatively impactful on patients’ daily lives, health systems, and society. However, with a better understanding of the gaps in knowledge and technological advances in IBS’s pathophysiology, management, and treatment options, there is optimism for the millions of people worldwide who are suffering from the debilitating effects of IBS.

Keywords: irritable bowel syndrome, lifestyle, diet, functional gastrointestinal disorder

Procedia PDF Downloads 89

Authors: G. Tamilpavai, C. Vishnuppriya

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Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.

Keywords: bioinformatics, cancer motif, DNA, k-mers, Levenshtein distance, SOM

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

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Authors: Eric Hawkinson, Edgaras Artemciukas

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This study outlines iterations of designing mobile augmented reality (MAR) applications for tourism specific contexts. Using a design based research model, several cycles of development to implementation were analyzed and refined upon with the goal of building a MAR platform that would facilitate the creation of augmented tours and environments by non-technical users. The project took on several stages, and through the process, a simple framework was begun to be established that can inform the design and use of MAR applications for tourism contexts. As a result of these iterations of development, a platform was developed that can allow novice computer users to create augmented tourism environments. This system was able to connect existing tools in widespread use such as Google Forms and connect them to computer vision algorithms needed for more advanced augmented tourism environments. The study concludes with a discussion of this MAR platform and reveals design elements that have implications for tourism contexts. The study also points to future case uses and design approaches for augmented tourism.

Keywords: augmented tourism, augmented reality, user experience, mobile design, e-tourism

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Authors: Mohamad Bagher Heidari, Hefzollah Mohammadian

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New discoveries in materials on the nanometer-length scale are expected to play an important role in addressing ongoing and future challenges in the field of communication. Devices and systems for ultra-high speed short and long range communication links, portable and power efficient computing devices, high-density memory and logics, ultra-fast interconnects, and autonomous and robust energy scavenging devices for accessing ambient intelligence and needed information will critically depend on the success of next-generation emerging nonmaterials and devices. This article presents some exciting recent developments in nonmaterials that have the potential to play a critical role in the development and transformation of future intelligent communication and telecom networks in the gas refinery. The industry is benefiting from nanotechnology advances with numerous applications including those in smarter sensors, logic elements, computer chips, memory storage devices, optoelectronics.

Keywords: nonmaterial, intelligent communication, nanoscale, nanophotonic, telecom

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Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

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Authors: Randa Boukhris, Alima Damak, Dorra Sellami

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Fingervein biometric authentication is one of the most popular and accurate technologies. However, low cost embedded solution is still an open problem. In this paper, a real-time implementation of fingervein recognition process embedded in Raspberry-Pi has been proposed. The use of Raspberry-Pi reduces overall system cost and size while allowing an easy user interface. Implementation of a target technology has guided to opt some specific parallel and simple processing algorithms. In the proposed system, we use four structural directional kernel elements for filtering finger vein images. Then, a Top-Hat and Bottom-Hat kernel filters are used to enhance the visibility and the appearance of venous images. For feature extraction step, a simple Local Directional Code (LDC) descriptor is applied. The proposed system presents an Error Equal Rate (EER) and Identification Rate (IR), respectively, equal to 0.02 and 98%. Furthermore, experimental results show that real-time operations have good performance.

Keywords: biometric, Bottom-Hat, Fingervein, LDC, Rasberry-Pi, ROI, Top-Hat

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Authors: Annabelle Onyefulu-Kingston

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One of the major purposes of AI today is to evaluate and analyze millions of micro and macro data in order to determine what is relevant in a particular case and proffer it in an adequate manner. Microdata, as far as it relates to AI in international arbitration, is the millions of key issues specifically mentioned by either one or both parties or by their counsels, arbitrators, or arbitral tribunals in arbitral proceedings. This can be qualifications of expert witness and admissibility of evidence, amongst others. Macro data, on the other hand, refers to data derived from the resolution of the dispute and, consequently, the final and binding award. A notable example of this includes the rationale of the award and specific and general damages awarded, amongst others. This paper aims to critically evaluate and analyze the possibility of technological inclusion in international arbitration. This research will be imploring the qualitative method by evaluating existing literature on the consequence of applying AI to both micro and macro data in international arbitration, and how this can be of assistance to parties, counsels, and arbitrators.

Keywords: AI-based technologies, algorithms, arbitrators, international arbitration

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Authors: Zi-Han Li, Zi-Jun Sun, Dong-Yuan Chang, Li Zhu, Min Chen, Ming-Hui Zhao

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Aims: A genome-wide association study (GWAS) reported that patients with the rs55703767 minor allele in collagen type IV α3 chain encoding gene COL4A3 showed protection against diabetic kidney disease (DKD) in type 1 diabetes mellitus (T1DM). However, the role of rs55703767 in type 2 DKD has not been elucidated. The aim of the current study was to investigate the association between COL4A3 variant rs55703767 and DKD risk in Chinese patients with type 2 diabetes mellitus (T2DM). Methods: This nested case-control study was performed on 1311 patients who had T2DM for at least 10 years, including 580 with DKD and 731 without DKD. We detected the genotypes of all patients by TaqMan SNP Genotyping Assay and analyzed the association between COL4A3 variant rs55703767 and DKD risk. Results: Genetic analysis revealed that there was no significant difference between T2DM patients with DKD and those without DKD regarding allele or genotype frequencies of rs55703767, and the effect of this variant was not hyperglycemia specific. Conclusion: Our findings suggested that there was no detectable association between the COL4A3 variant rs55703767 and the susceptibility to DKD in the Chinese T2DM population.

Keywords: collagen type IV α3 chain, gene polymorphism, type 2 diabetes, diabetic kidney disease

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Authors: Nargiz Ismayilova

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In Kamal Abdulla's "The Adventure of Mysteries", the plot develops on two parallel lines. While reading the work, the future looks hazy on the background of the present and the past. It is impossible to predict the end of the work in particular. This can be considered the success of the author. The novel has reflected the features of postmodernism. The novel is characterized by a richness of intertwined plots, themes, meta- submission, device (fiction) typical of postmodern prose technique. The introduction and progress of the work takes the reader to the place, which is an unrecognizable unknown for him but at the same time, its native for him very well. Parts of the novel, divided into chapter techniques, force the reader to distinguish mystical repetitions from the artistic circulation of reality. This makes people think directly. Intertextual communication and the variety of fiction, intelligence, and informativeness determine the perspective of the exemplary reader. As is well known, “postmodern novels, which often use intertextual communication and superstructure techniques, focus on expression rather than on the subject, and benefit from history by combining fiction with historical facts, are able to attract attention with their extraordinary foreign fiction.

Keywords: Kamal Abdulla, postmodernism, parallelism, labyrinth, comparison, novel

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Authors: Zahra Salehi Shahrbabaki, Zahra Chavoshzadeh, Fahimeh Abdollahimajd, Samin Sharafian, Tolue Mahdavi, Mahnaz Jamee

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Background: Inborn errors of immunity (IEIs) are monogenic diseases of the immune the system with broad clinical manifestations. Despite the increasing genetic advancements, the diagnosis of IEIs still leans on clinical diagnosis. Dermatologic manifestations are observed in a large number of IEI patients and can lead to proper approach, prompt intervention and improved prognosis. Methods: This cross-sectional study was carried out between 2018 and 2020 on IEIs at a Children's tertiary care center in Tehran, Iran. Demographic details (including age, sex, and parental consanguinity), age at onset of symptoms and family history of IEI with were recorded. Results :212 patients were included. Cutaneous findings were reported in (95 ,44.8%) patients. and 61 of 95 (64.2%) reported skin lesions as the first clinical presentation. Skin infection (69, 72.6%) was the most frequent cutaneous manifestation, followed by an eczematous rash (24, 25 %). Conclusions: Skin manifestations are common feature in IEI patients and can be readily recognizable by healthcare providers. This study tried to provide information on prognostic consequences.

Keywords: primary immuno deficiency, inborn errror of metabolism, skin manifestation, skin infection

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Authors: Abdullah M. Almeshal, Mohammad R. Alenezi, Muhammad Moaz

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In this paper, we discuss the performance of applying hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation algorithm on an intelligent controller for a differential drive robot. A unicycle class of differential drive robot is utilised to serve as a basis application to evaluate the performance of the HSDBC algorithm. A hybrid fuzzy logic controller is developed and implemented for the unicycle robot to follow a predefined trajectory. Trajectories of various frictional profiles and levels were simulated to evaluate the performance of the robot at different operating conditions. Controller gains and scaling factors were optimised using HSDBC and the performance is evaluated in comparison to previously adopted optimisation algorithms. The HSDBC has proven its feasibility in achieving a faster convergence toward the optimal gains and resulted in a superior performance.

Keywords: differential drive robot, hybrid fuzzy controller, optimization, path tracking, unicycle robot

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Adeniran K. Ademuwagun, Alastair Allen

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The relationship dependence between RSS and distance in an enclosed environment is an important consideration because it is a factor that can influence the reliability of any localization algorithm founded on RSS. Several algorithms effectively reduce the variance of RSS to improve localization or accuracy performance. Our proposed algorithm essentially avoids this pitfall and consequently, its high adaptability in the face of erratic radio signal. Using 3 anchors in close proximity of each other, we are able to establish that RSS can be used as reliable indicator for localization with an acceptable degree of accuracy. Inherent in this concept, is the ability for each prospective anchor to validate (guarantee) the position or the proximity of the other 2 anchors involved in the localization and vice versa. This procedure ensures that the uncertainties of radio signals due to multipath effects in enclosed environments are minimized. A major driver of this idea is the implicit topological relationship among sensors due to raw radio signal strength. The algorithm is an area based algorithm; however, it does not trade accuracy for precision (i.e the size of the returned area).

Keywords: anchor nodes, centroid algorithm, communication graph, radio signal strength

Procedia PDF Downloads 508

Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Salami Akeem Olanrewaju

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The transportation models or problems are primarily concerned with the optimal (best possible) way in which a product produced at different factories or plants (called supply origins) can be transported to a number of warehouses or customers (called demand destinations). The objective in a transportation problem is to fully satisfy the destination requirements within the operating production capacity constraints at the minimum possible cost. The objective of this study is to determine ways of minimizing transport cost in order to maximum profit. Data were gathered from the records of the Distribution Department of 7-Up Bottling Company Plc. Ilorin, Kwara State, Nigeria. The data were analyzed using SPSS (Statistical Package for Social Sciences) while applying the three methods of solving a transportation problem. The three methods produced the same results; therefore, any of the method can be adopted by the company in transporting its final products to the wholesale dealers in order to minimize total production cost.

Keywords: cost minimization, resources utilization, distribution system, allocation problem

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Fanqiang Kong, Chending Bian

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In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined l_2,p-norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy.

Keywords: hyperspectral unmixing, joint-sparse, low-rank representation, abundance estimation

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Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

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The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: artificial neural networks, cellular automata, decision support system, pattern recognition

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Authors: Yahia I. Mohamed, Ahmed I. Marzouk, Mohamed A. Yacout

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Aim of this work was to study the genetic basis for oil accumulation in olive fruit via tracking DGAT2 (Diacylglycerol acyltransferase type-2) gene in three Egyptian Origen Olive cultivars namely Toffahi, Hamed and Maraki using molecular marker techniques and bioinformatics tools. Results illustrate that, firstly: specific genomic band of Maraki cultivars was identified as DGAT2 (Diacylglycerol acyltransferase type-2) and identical for this gene in Olea europaea with 100 % of similarity. Secondly, differential genomic band of Maraki cultivars which produced from RAPD fingerprinting technique reflected predicted distinguished sequence which identified as DGAT2 (Diacylglycerol acyltransferase type-2) in Fragaria vesca subsp. Vesca with 76% of sequential similarity. Third and finally, specific genomic specific band of Hamed cultivars was indentified as two fragments, 1-Olea europaea cultivar Koroneiki diacylglycerol acyltransferase type 2 mRNA, complete cds with two matches regions with 99% or 2-PREDICTED: Fragaria vesca subsp. vesca diacylglycerol O-acyltransferase 2-like (LOC101313050), mRNA with 86% of similarity.

Keywords: Olea europaea, fingerprinting, diacylglycerol acyltransferase type-2 (DGAT2), Egypt

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Authors: Iman Dwi Almunandar, Nellawaty A. Tewu, Anshari Al Ghaniyy

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The emergence of academic procrastination behavior among students in Indonesian, especially the students of Faculty of Psychology at YARSI University becomes a habit to be underestimated, so often interfere with the effectiveness of learning process. The lecturers at the Faculty of Psychology YARSI University have very often warned students to be able to do and collect assignments accordance to predetermined deadline. However, they are still violated it. According to researchers, this problem needs to do a proper training for the solution to minimize academic procrastination behavior on students. In this study, researchers conducted analyze needs for deciding whether need the training or not. Number of sample is 30 respondents which being choose with a simple random sampling. Measurement of academic procrastination behavior is using the theory by McCloskey (2011), there are six dimensions: Psychological Belief about Abilities, Distractions, Social Factor of Procrastination, Time Management, Personal Initiative, Laziness. Methods of analyze needs are using Questioner, Interview, Observations, Focus Group Discussion (FGD), Intelligence Tests. The result of analyze needs shows that psychology students generation of 2015 at the Faculty of Psychology YARSI University need for training on Time Management.

Keywords: procrastination, psychology, analyze needs, behavior

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Authors: Gopi Kerekere Rangaraju, Madhu Puttegowda

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Natural fibres composites include coir, jute, bagasse, cotton, bamboo, and hemp. Natural fibers come from plants. These fibers contain lingo cellulose in nature. Natural fibers are eco-friendly; lightweight, strong, renewable, cheap, and biodegradable. The natural fibers can be used to reinforce both thermosetting and thermoplastic matrices. Thermosetting resins such as epoxy, polyester, polyurethane, and phenolic are commonly used composites requiring higher performance applications. They provide sufficient mechanical properties, in particular, stiffness and strength at acceptably low-price levels. Recent advances in natural fibers development are genetic engineering. The composites science offers significant opportunities for improved materials from renewable resources with enhanced support for global sustainability. Natural fibers composites are attractive to industry because of their low density and ecological advantages over conventional composites. These composites are gaining importance due to their non-carcinogenic and bio-degradable nature. Natural fibers composites are a very costeffective material, especially in building and construction, packaging, automobile and railway coach interiors, and storage devices. These composites are potential candidates for the replacement of high- cost glass fibers for low load bearing applications. Natural fibers have the advantages of low density, low cost, and biodegradability

Keywords: PMC, basalt, coir, carbon fibers

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Authors: Jinkoo Kim, Minsung Kim

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This study develops a hybrid seismic energy dissipation device composed of a viscoelastic damper and a steel slit damper connected in parallel. A cyclic loading test is conducted on a test specimen to validate the seismic performance of the hybrid damper. Then a moment-framed model structure is designed without seismic load so that it is retrofitted with the hybrid dampers. The model structure is transformed into an equivalent simplified system to find out optimum story-wise damper distribution pattern using genetic algorithm. The effectiveness of the hybrid damper is investigated by fragility analysis and the life cycle cost evaluation of the structure with and without the dampers. The analysis results show that the model structure has reduced probability of reaching damage states, especially the complete damage state, after seismic retrofit. The expected damage cost and consequently the life cycle cost of the retrofitted structure turn out to be significantly small compared with those of the original structure. Acknowledgement: This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R & D program (N043100016).

Keywords: seismic retrofit, slit dampers, friction dampers, hybrid dampers

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Authors: Elham Jelodari Mamaghani, Christian Prins, Haoxun Chen

Abstract:

In this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver.

Keywords: centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA

Procedia PDF Downloads 392

Authors: Norfiza Ibrahim, Norshuhada Shiratuddin, Siti Mahfuzah Sarif

Abstract:

Decision-making system is used to facilitate people in making the right choice for their important daily activities. For the youth, proper guidance in making important decisions is needed. Their skills in decision-making aid decisions will indirectly affect their future. For that reason, this study focuses on the intelligent aspects in the development of intelligent decision support application. The aid apparently integrates Personality Traits (PT) and Multiple Intelligence (MI) data in development of a computerized personal decision aid for youth named as Youth Personal Decision Aid (Youth PDA). This study is concerned with the aid’s helpfulness based on the hybrid intelligent process. There are four main items involved which are reliability, decision making effort, confidence, as well as decision process awareness. Survey method was applied to the actual user of this system, namely the school and the Institute of Higher Education (IPT)’s students. An establish instrument was used to evaluate the study. The results of the analysis and findings in the assessment indicates a high mean value of the four dimensions in helping Youth PDA to be accepted as a useful tool for the youth in decision-making.

Keywords: decision support, multiple intelligent, personality traits, youth personal decision aid

Procedia PDF Downloads 632

Authors: Aritras Roy, Rinku Mukherjee

Abstract:

The current study couples a quasi-steady Vortex Lattice Method and a camber correcting technique, ‘Decambering’ for unsteady post-stall flow prediction. The wake is force-free and discrete such that the wake lattices move with the free-stream once shed from the wing. It is observed that the time-averaged unsteady coefficient of lift sees a relative drop at post-stall angles of attack in comparison to its steady counterpart for some angles of attack. Multiple solutions occur at post-stall and three different algorithms to choose solutions in these regimes show both unsteadiness and non-convergence of the iterations. The distribution of coefficient of lift on the wing span also shows sawtooth. Distribution of vorticity changes both along span and in the direction of the free-stream as the wake develops over time with distinct roll-up, which increases with time.

Keywords: post-stall, unsteady, wing, aerodynamics

Procedia PDF Downloads 370

Authors: Melissa C. Monney

Abstract:

Psychological testing has been of interest to researchers for many years as useful tools in assessing and diagnosing various disorders as well as to assist in understanding human behavior. However, for over 20 years now, researchers and laypersons alike have been interested in using them for other purposes, such as determining factors in employee selection, promotion, and even termination. In recent years, psychological assessments have been useful in facilitating workplace decision processing, regarding employee circulation within organizations. This literature review explores four of the most commonly used psychological tests in workplace environments, namely cognitive ability, emotional intelligence, integrity, and personality tests, as organizations have used these tests to assess different factors of human behavior as predictive measures of future employee behaviors. The findings suggest that while there is much controversy and debate regarding the validity and reliability of these tests in workplace settings as they were not originally designed for these purposes, the use of such assessments in the workplace has been useful in decreasing costs and employee turnover as well as increase job satisfaction by ensuring the right employees are selected for their roles.

Keywords: cognitive ability, personality testing, predictive validity, workplace behavior

Procedia PDF Downloads 242