Search results for: harmony search algorithms

Authors: Hernández Eva Selene, Reyna Mary Carmen, Rivera Héctor, Barragán Irving

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We propose an approach that jointly addresses the layout of a facility and the scheduling of a sequence of jobs. In real production, these two problems are interrelated. However, they are treated separately in the literature. Our approach is an extension of the job shop problem with transportation delay, where the location of the machines is selected among possible sites. The model minimizes the makespan, using the short processing times rule with two algorithms; the first one considers all the permutations for the location of machines, and the second only a heuristic to select some specific permutations that reduces computational time. Some instances are proved and compared with literature.

Keywords: layout problem, job shop scheduling problem, concurrent scheduling and layout problem, metaheuristic

Procedia PDF Downloads 593

Authors: Malarvizhi, Venkatesan

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This paper summarizes about the design and optimization of solar powered unmanned aerial vehicle. The purpose of this research is to increase the range and endurance. It can be used for environmental research, aerial photography, search and rescue mission and surveillance in other planets. The ultimate aim of this research is to design and analyze the solar powered plane in order to detect lift, drag and other parameters by using cfd analysis. Similarly the numerical investigation has been done to compare the results of earth’s atmosphere to the mars atmosphere. This is the approach made to check whether the solar powered plane is possible to glide in the planet mars by using renewable energy (i.e., solar energy).

Keywords: optimization, range, endurance, surveillance, lift and drag parameters

Procedia PDF Downloads 447

Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

Procedia PDF Downloads 483

Authors: Matthew T. Wilson

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This paper covers a selection of research utilizing grammar-based genetic programming, and illustrates how context-free grammar can be used to constrain genetic programming. It focuses heavily on grammatical evolution, one of the most popular variants of grammar-based genetic programming, and the way its operators and terminals are specialized and modified from those in genetic programming. A variety of implementations of grammatical evolution for general use are covered, as well as research each focused on using grammatical evolution or grammar-based genetic programming on a single application, or to solve a specific problem, including some of the classically considered genetic programming problems, such as the Santa Fe Trail.

Keywords: context-free grammar, genetic algorithms, genetic programming, grammatical evolution

Procedia PDF Downloads 168

Authors: Muhammad Yasir Tarar

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Background: Intraarticular steroid injections are an effective option in alleviating the symptoms of conditions like osteoarthritis, rheumatoid arthritis, crystal arthropathy, and rotator cuff tendinopathy. Most of these injections are conducted in the elderly who are on polypharmacy, including anticoagulants at times. Up to 6% of patients aged 80-84 years have been reported to be taking Warfarin. The literature availability on safety quotient for patients undergoing intraarticular injections on Warfarin is scarce. It has remained debatable over the years which approach is safe for these patients. Continuing warfarin has a theoretical bleeding risk, and stopping it can lead to even severe life-threatening thromboembolic events in high-risk patients. Objectives: To evaluate the risk of bleeding complications in patients on warfarin undergoing intraarticular injections or arthrocentesis. Study Design & Methods: A literature search of MEDLINE (1946 to present), EMBASE (1974 to present), and Cochrane CENTRAL (1988 to present) databases were conducted using any combination of the keywords, Injection, Knee, Shoulder, Joint, Intraarticular, arthrocentesis, Warfarin, and Anticoagulation in November 2020 for articles published in any language with no publication year limit. The study inclusion criteria included reporting on the rate of bleeding complications following injection of the knee or shoulder in patients on warfarin treatment. Randomized control trials and prospective and retrospective study designs were included. An electronic standardized Performa for data extraction was made. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) the methodology was used. The articles were appraised using the methodological index for nonrandomized studies. The Cochrane Risk of Bias Tool used to assess the risk of bias in included RCTs and the MINORS tool for assessment of bias in observational studies. Results: The search of databases resulted in a total of 852 articles. Relevant articles as per the inclusion criteria were shortlisted, 7 articles deemed suitable to be include. A total of 1033 joints sample size was undertaken with specified knee and shoulder joints of a total of 820. Only 6 joints had bleeding complications, 5 early bleeding at the time of injection or aspiration, and one late bleeding complication with INR of 5, additionally, 2 patients complained of bruising, 3 of pain, and 1 managed for infection. Conclusions: The results of the metanalysis show that it is relatively safe to perform intraarticular injections in patients on Warfarin regardless of the INR range.

Keywords: arthrocentesis, warfarin, bleeding, injection

Procedia PDF Downloads 66

Authors: Diogo Santos, Nelson Zagalo, Carla Morais

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No Man’s Sky (NMS) is a sci-fi video game about survival and exploration where players fly spaceships, search for elements, and use them to survive. NMS isn’t a serious game, and not all the science in the game is presented with scientific evidence. To find how players felt about the scientific content in the game and how they perceive the chemistry in it, a survey was sent to NMS’s players, from which were collected answers from 124 respondents from 23 countries. Chemophobia is still a phenomenon when chemistry or chemicals are a subject of discussion, but 68,9% of our respondents showed a positive attitude towards the presence of chemistry in NMS, with 57% stating that playing the video game motivated them to know more about science. 8% of the players stated that NMS often prompted conversations about the science in the video game between them and teachers, parents, or friends. These results give us ideas on how an entertainment game can potentially help scientists, educators, and science communicators reach a growing, evolving, vibrant, diverse, and demanding audience.

Keywords: digital games, science communication, chemistry, informal learning, No Man’s Sky

Procedia PDF Downloads 90

Authors: Sammani Danwawu Abdullahi

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Vertex Enumeration Algorithms explore the methods and procedures of generating the vertices of general polyhedra formed by system of equations or inequalities. These problems of enumerating the extreme points (vertices) of general polyhedra are shown to be NP-Hard. This lead to exploring how to count the vertices of general polyhedra without listing them. This is also shown to be #P-Complete. Some fully polynomial randomized approximation schemes (fpras) of counting the vertices of some special classes of polyhedra associated with Down-Sets, Independent Sets, 2-Knapsack problems and 2 x n transportation problems are presented together with some discovered open problems.

Keywords: counting with uncertainties, mathematical programming, optimization, vertex enumeration

Procedia PDF Downloads 337

Authors: Abdulmajid Mukhtar Afat

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This paper aims at introducing finite automata theory, the different ways to describe regular languages and create a program to implement the subset construction algorithms to convert nondeterministic finite automata (NFA) to deterministic finite automata (DFA). This program is written in c++ programming language. The program reads FA 5tuples from text file and then classifies it into either DFA or NFA. For DFA, the program will read the string w and decide whether it is acceptable or not. If accepted, the program will save the tracking path and point it out. On the other hand, when the automation is NFA, the program will change the Automation to DFA so that it is easy to track and it can decide whether the w exists in the regular language or not.

Keywords: finite automata, subset construction, DFA, NFA

Procedia PDF Downloads 417

Authors: Chantelle Francis, Karen McKenzie, Charlotte Emmerson

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In the context of the rise in mental health difficulties amongst pupils, this review explores the factors that have been indicated as affecting psychological well-being in mainstream school contexts. Search terms relating to school-based psychological well-being were entered into five databases, and twenty-two studies were included in the review. The results suggested that pupil psychological well-being is affected by both direct and indirect factors. The former included a sense of belonging and inclusion, relationships with teachers, and academic attainment. The latter included family socioeconomic status, whole-school approaches, and individual differences factors, such as gender and Special Educational Needs. The implications for policymakers and practitioners are discussed.

Keywords: psychological wellbeing, mainstream schools, special educational needs, school-based wellbeing

Procedia PDF Downloads 105

Authors: Hussain K. Chaiel

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The development in the field of the digital signal processing (DSP) and the microelectronics technology reduces the complexity of the iterative algorithms that need large number of arithmetic operations. Virtex-Field Programmable Gate Arrays (FPGAs) are programmable silicon foundations which offer an important solution for addressing the needs of high performance DSP designer. In this work, Virtex-7 FPGA technology is used to implement an iterative algorithm to estimate the earthquake location. Simulation results show that an implementation based on block RAMB36E1 and DSP48E1 slices of Virtex-7 type reduces the number of cycles of the clock frequency. This enables the algorithm to be used for earthquake prediction.

Keywords: DSP, earthquake, FPGA, iterative algorithm

Procedia PDF Downloads 372

Authors: F. Tang, S. Boskovich, A. Raheja, Z. Aliyazicioglu, S. Bhandari, N. Tsuchiya

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Calpoly Autonomous Transportation Experience (CATE) is a driverless vehicle that we are developing to provide safe, accessible, and efficient transportation of passengers throughout the Cal Poly Pomona campus for events such as orientation tours. Unlike the other self-driving vehicles that are usually developed to operate with other vehicles and reside only on the road networks, CATE will operate exclusively on walk-paths of the campus (potentially narrow passages) with pedestrians traveling from multiple locations. Safety becomes paramount as CATE operates within the same environment as pedestrians. As driverless vehicles assume greater roles in today’s transportation, this project will contribute to autonomous driving with pedestrian traffic in a highly dynamic environment. The CATE project requires significant interdisciplinary work. Researchers from mechanical engineering, electrical engineering and computer science are working together to attack the problem from different perspectives (hardware, software and system). In this abstract, we describe the software aspects of the project, with a focus on the requirements and the major components. CATE shall provide a GUI interface for the average user to interact with the car and access its available functionalities, such as selecting a destination from any origin on campus. We have developed an interface that provides an aerial view of the campus map, the current car location, routes, and the goal location. Users can interact with CATE through audio or manual inputs. CATE shall plan routes from the origin to the selected destination for the vehicle to travel. We will use an existing aerial map for the campus and convert it to a spatial graph configuration where the vertices represent the landmarks and edges represent paths that the car should follow with some designated behaviors (such as stay on the right side of the lane or follow an edge). Graph search algorithms such as A* will be implemented as the default path planning algorithm. D* Lite will be explored to efficiently recompute the path when there are any changes to the map. CATE shall avoid any static obstacles and walking pedestrians within some safe distance. Unlike traveling along traditional roadways, CATE’s route directly coexists with pedestrians. To ensure the safety of the pedestrians, we will use sensor fusion techniques that combine data from both lidar and stereo vision for obstacle avoidance while also allowing CATE to operate along its intended route. We will also build prediction models for pedestrian traffic patterns. CATE shall improve its location and work under a GPS-denied situation. CATE relies on its GPS to give its current location, which has a precision of a few meters. We have implemented an Unscented Kalman Filter (UKF) that allows the fusion of data from multiple sensors (such as GPS, IMU, odometry) in order to increase the confidence of localization. We also noticed that GPS signals can easily get degraded or blocked on campus due to high-rise buildings or trees. UKF can also help here to generate a better state estimate. In summary, CATE will provide on-campus transportation experience that coexists with dynamic pedestrian traffic. In future work, we will extend it to multi-vehicle scenarios.

Keywords: driverless vehicle, path planning, sensor fusion, state estimate

Procedia PDF Downloads 128

Authors: Srivatsav Sanjay Sridhar, Asvitha Raja, Prathit Kalra, Soni Gupta

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Database management is a system that keeps track of details about a person in an organisation. Not a lot of orphanages these days are shifting to a computer and program-based system, but unfortunately, most have only pen and paper-based records, which not only consumes space but it is also not eco-friendly. It comes as a hassle when one has to view a record of a person as they have to search through multiple records, and it will consume time. This program will organise all the data and can pull out any information about anyone whose data is entered. This is also a safe way of storage as physical data gets degraded over time or, worse, destroyed due to natural disasters. In this developing world, it is only smart enough to shift all data to an electronic-based storage system. The program comes with all features, including creating, inserting, searching, and deleting the data, as well as printing them.

Keywords: database, orphans, programming, C⁺⁺

Procedia PDF Downloads 128

Authors: Adilson C. Barros, Kayo Okazaki, Valter Arthur

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The radio-protective substances protect the organism from ionizing radiation when previously ingested. Synthetic radio-protectives produce unpleasant side effects and are expensive. This article reports the search for natural radio-protective agents in foods, whose occurrence is widespread, costs are lower and the side effects are non-existent. In this work, we studied the eggplant, a food widely used in Brazil, comparing the radiosensitivity of insects reared on diet eggplant and outside this diet. The eggplant causes change in LD50 parameter of insects population but the response curve needs to be better shaped to conclude something about radioprotection. What we can see is that it seems to contain some radiomodifier substance.

Keywords: radioprotector, radiobiology, Solanun melongena L., Lasioderma serricorne

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Authors: Ariyan S. Araghi, Guy C. Jackson, Stephen J. Bonsor

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Materials & Methods: A systematic literature search was undertaken using pre-determined inclusion and exclusion criteria. This review followed the Preferred Reporting Items for Systemic Reviews and Meta-Analysis (PRISMA) statement. Several databases were used to search for randomised control trials and longitudinal cohort studies, which were published less than thirty years ago. A total of 54 studies met the predefined inclusion criteria. Four studies reviewed the success, survival, and failure characteristics of zirconia framework resin retained bridges, whilst two reviewed non-precious metal resin retained bridges. Results: The analysis of the studies revealed an overall survival rate of 95.9% for zirconia-based restorations compared to 90.7% for non-precious metal frameworks. Non-precious metal resin retained bridges displayed a higher overall failure rate of 11.9% compared to 4.6% for zirconia-based restorations in the analysed papers. The most frequent complications were wing debonding for the non-precious metal wing group, whereas substructure fracture and veneering ceramic fracture were more prevalent for the zirconia arm of the study. Conclusion: Both types of resin retained bridges provide effective medium to long-term survival. Zirconia-based frameworks will provide marginally increased success and survival and greatly improved aesthetics. However, catastrophic failure is more likely with zirconia-based restorations. Non-precious metal is time tested but performs worse than its zirconia counterpart with regards to longevity; it does not exhibit the same framework fractures as zirconia. Cement choice and attention to the adhesive bonding systems used appear to be paramount to restoration longevity with both restoration subtypes. Furthermore, improved longevity can be seen when air particle abrasion is incorporated into the adhesive protocol. Within the limitations of this study, it has been determined that zirconia-based resin retained bridges can be effectively used in anterior cantilever bridges. Clinical Significance: Zirconia-based resin retained bridges have been demonstrating promising results in terms of improved success and survival characteristics, together with improved aesthetics when compared to non-precious metal winged resin retained bridges. Their popularity is increasing in the age of digital dentistry as many restorations are manufactured using such technology. It is essential that clinicians understand the limitations of each material type and principles of adhesion to ensure restoration longevity.

Keywords: resin retained bridge, fixed partial denture, zirconia bridge, adhesive bridge

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

Procedia PDF Downloads 399

Authors: Aya Mousa, Mahmoud Alsaheb

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Question Answering System (QAS) aims to provide the most suitable answer to the user's question in any natural language. In the recent future, it will be a future version of web search. Much research has already been done on answering Arabic factoid questions and achieved good accuracy. In contrast, the progress in research on Arabic non-factoid question answering is still immature. In this survey, we summarize, discuss, and compare the existing Arab non-factoid question-answering systems to identify the limitations and the achievements that were accomplished. Furthermore, we investigate the challenges in developing non-factoid Arabic QAS and the possible future improvements. The survey is written to help the researchers to understand the field of Arabic non-factoid QAS and to motivate them to utilize different approaches to develop and enhance the Non-factoid Arabic QAS

Keywords: Arabic question answering system, non-factoid question answering, Arabic NLP, question answering

Procedia PDF Downloads 84

Authors: A. Graa, I. Ziane, F. Benhamida, S. Souag

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This paper presents a comparative analysis study of an efficient and reliable quadratic programming (QP) to solve economic load dispatch (ELD) problem with considering transmission losses in a power system. The proposed QP method takes care of different unit and system constraints to find optimal solution. To validate the effectiveness of the proposed QP solution, simulations have been performed using Algerian test system. Results obtained with the QP method have been compared with other existing relevant approaches available in literatures. Experimental results show a proficiency of the QP method over other existing techniques in terms of robustness and its optimal search.

Keywords: economic dispatch, quadratic programming, Algerian network, dynamic load

Procedia PDF Downloads 547

Authors: Benhamouda Sara, Guezouli Larbi

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Nowadays, the dissemination of information touches the distributed world, where selecting the relevant servers to a user request is an important problem in distributed information retrieval. During the last decade, several research studies on this issue have been launched to find optimal solutions and many approaches of collection selection have been proposed. In this paper, we propose a new collection selection approach that takes into consideration the number of documents in a collection that contains terms of the query and the weights of those terms in these documents. We tested our method and our studies show that this technique can compete with other state-of-the-art algorithms that we choose to test the performance of our approach.

Keywords: distributed information retrieval, relevance, server selection, collection selection

Procedia PDF Downloads 286

Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

Procedia PDF Downloads 498

Authors: Yi-Cheng Tian, Miin-Shen Yang

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The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

Procedia PDF Downloads 497

Authors: Alsu F. Saifitdinova, Alexey S. Komissarov, Svetlana A. Galkina, Elena I. Koshel, Maria M. Kulak, Stephen J. O'Brien, Elena R. Gaginskaya

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The mystery of sex determination is one of the most ancient and still not solved until the end so far. In many species, sex determination is genetic and often accompanied by the presence of dimorphic sex chromosomes in the karyotype. Genomic sequencing gave the information about the gene content of sex chromosomes which allowed to reveal their origin from ordinary autosomes and to trace their evolutionary history. Female-specific W chromosome in birds as well as mammalian male-specific Y chromosome is characterized by the degeneration of gene content and the accumulation of repetitive DNA. Tandem repeats complicate the analysis of genomic data. Despite the best efforts chicken W chromosome assembly includes only 1.2 Mb from expected 55 Mb. Supplementing the information on the sex chromosome composition not only helps to complete the assembly of genomes but also moves us in the direction of understanding of the sex-determination systems evolution. A whole-genome survey to the assembly Gallus_gallus WASHUC 2.60 was applied for repeats search in assembled genome and performed search and assembly of high copy number repeats in unassembled reads of SRR867748 short reads datasets. For cytogenetic analysis conventional methods of fluorescent in situ hybridization was used for previously cloned W specific satellites and specifically designed directly labeled synthetic oligonucleotide DNA probe was used for bioinformatically identified repetitive sequence. Hybridization was performed with mitotic chicken chromosomes and manually isolated giant meiotic lampbrush chromosomes from growing oocytes. A novel chicken W specific satellite (GGAAA)n which is not co-localizes with any previously described classes of W specific repeats was identified and mapped with high resolution. In the composition of autosomes this repeat units was found as a part of upstream regions of gonad specific protein coding sequences. These findings may contribute to the understanding of the role of tandem repeats in sex specific differentiation regulation in birds and sex chromosome evolution. This work was supported by the postdoctoral fellowships from St. Petersburg State University (#1.50.1623.2013 and #1.50.1043.2014), the grant for Leading Scientific Schools (#3553.2014.4) and the grant from Russian foundation for basic researches (#15-04-05684). The equipment and software of Research Resource Center “Chromas” and Theodosius Dobzhansky Center for Genome Bioinformatics of Saint Petersburg State University were used.

Keywords: birds, lampbrush chromosomes, sex chromosomes, tandem repeats

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Sardar Ameer Akram Khan, Shahzad Amin Sheikh

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Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results.

Keywords: blind equalizations, constant modulus algorithm, multi-modulus algorithm, recursive least square algorithm, quadrature amplitude modulation (QAM)

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Authors: Gagnesh Kumar, Nitin Gupta

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Ternary Content Addressable Memory cells are mainly popular in network routers for packet forwarding and packet classification, but they are also useful in a variety of other applications that require high-speed table look-up. The main TCAM-design challenge is to decrease the power consumption associated with the large amount of parallel active circuitry, without compromising with speed or memory density. Furthermore, when the channel length decreases, leakage power becomes more significant, and it can even dominate dynamic power at lower technologies. In this paper, we propose a TCAM-design technique, called Virtual Power Supply technique that reduces the leakage by a substantial amount.

Keywords: match line (ML), search line (SL), ternary content addressable memory (TCAM), Leakage power (LP)

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Authors: N. Tugrul, E. Sariburun, F. T. Senberber, A. S. Kipcak, E. Moroydor Derun, S. Piskin

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Boron-gypsum is a waste which occurs in the boric acid production process. In this study, the boron content of this waste is evaluated for the use in synthesis of magnesium borates and such evaluation of this kind of waste is useful more than storage or disposal. Magnesium borates, which are a sub-class of boron minerals, are useful additive materials for the industries due to their remarkable thermal and mechanical properties. Magnesium borates were obtained hydrothermally at different temperatures. Novelty of this study is the search of the solution density effects to magnesium borate synthesis process for the increasing the possibility of boron-gypsum usage as a raw material. After the synthesis process, products are subjected to XRD and FT-IR to identify and characterize their crystal structure, respectively.

Keywords: boron-gypsum, hydrothermal synthesis, magnesium borate, solution density

Procedia PDF Downloads 376

Authors: Petra Balla, Gabor Manhertz, Akos Antal

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Nowadays spinal deformities are very frequent problems among teenagers. Scheuermann disease is a one dimensional deformity of the spine, but it has prevalence over 11% of the children. A traditional technology, the moiré method was used by us for screening and diagnosing this type of spinal deformity. A LabVIEW program has been developed to evaluate the moiré pictures of patients with Scheuermann disease. Two different solutions were tested in this computer program, the extreme and the inflexion point calculation methods. Effects using these methods were compared and according to the results both solutions seemed to be appropriate. Statistical results showed better efficiency in case of the extreme search method where the average difference was only 6,09⁰.

Keywords: spinal deformity, picture evaluation, Moiré method, Scheuermann disease, curve detection, Moiré topography

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

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Authors: Anli Du Preez, James Bekker

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Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

Procedia PDF Downloads 317