Search results for: extra trees classifier

Authors: Yeonjeong Lee, Kyoung-jae Kim, Youngtae Kim

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This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.

Keywords: product recommender system, ensemble technique, association rules, decision tree, artificial neural networks

Procedia PDF Downloads 295

Authors: Victoria L. Chester, Usha Kuruganti

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The forest industry is a major economic sector of Canada and also one of the most dangerous industries for workers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence of injuries in forest workers related to manual labor. However, these machines have also created additional concerns, including a high machine operation learning curve, increased the length of the workday, repetitive strain injury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confined space. It is critical to obtain objective performance data for employers to develop appropriate work practices for this industry, however ergonomic field studies of this industry are lacking mainly due to the difficulties in obtaining comprehensive data while operators are cutting trees in the woods. The purpose of this study was to establish a measurement and experimental protocol to examine the effects of worker skill level and movement training speed (joystick crane speed) on harvesting performance using a forestry simulator. A custom wrist angle measurement device was developed as part of the study to monitor Euler angles during operation of the simulator. The device of the system consisted of two accelerometers, a Bluetooth module, three 3V coin cells, a microcontroller, a voltage regulator and an application software. Harvesting performance and crane data was provided by the simulator software and included tree to frame collisions, crane to tree collisions, boom tip distance, number of trees cut, etc. A pilot study of 3 operators with various skill levels was tested to identify factors that distinguish highly skilled operators from novice or intermediate operators. Dependent variables such as reaction time, math skill, past work experience, training movement speed (e.g. joystick control speeds), harvesting experience level, muscle activity, and wrist biomechanics were measured and analyzed. A 10-channel wireless surface EMG system was used to monitor the amplitude and mean frequency of 10 upper extremity muscles during pre and postperformance on the forestry harvest stimulator. The results of the pilot study showed inconsistent changes in median frequency pre-and postoperation, but there was the increase in the activity of the flexor carpi radialis, anterior deltoid and upper trapezius of both arms. The wrist sensor results indicated that wrist supination and pronation occurred more than flexion and extension with radial-ulnar rotation demonstrating the least movement. Overall, wrist angular motion increased as the crane speed increased from slow to fast. Further data collection is needed and will help industry partners determine those factors that separate skill levels of operators, identify optimal training speeds, and determine the length of training required to bring new operators to an efficient skill level effectively. In addition to effective and employment training programs, results of this work will be used for selective employee recruitment strategies to improve employee retention after training. Further, improved training procedures and knowledge of the physical and mental demands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimal work protocols.

Keywords: EMG, forestry, human factors, wrist biomechanics

Procedia PDF Downloads 147

Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

Procedia PDF Downloads 233

Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

Procedia PDF Downloads 119

Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

Procedia PDF Downloads 358

Authors: Yiwei Li, Boyu Tian, Mingyu Gao

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Hybrid main memory systems combine both performance and capacity advantages from heterogeneous memory technologies. With larger capacities, higher associativities, and finer granularities, hybrid memory systems currently exhibit significant metadata storage and lookup overheads for flexibly remapping data blocks between the two memory tiers. To alleviate the inefficiencies of existing designs, we propose Trimma, the combination of a multi-level metadata structure and an efficient metadata cache design. Trimma uses a multilevel metadata table to only track truly necessary address remap entries. The saved memory space is effectively utilized as extra DRAM cache capacity to improve performance. Trimma also uses separate formats to store the entries with non-identity and identity mappings. This improves the overall remap cache hit rate, further boosting the performance. Trimma is transparent to software and compatible with various types of hybrid memory systems. When evaluated on a representative DDR4 + NVM hybrid memory system, Trimma achieves up to 2.4× and on average 58.1% speedup benefits, compared with a state-of-the-art design that only leverages the unallocated fast memory space for caching. Trimma addresses metadata management overheads and targets future scalable large-scale hybrid memory architectures.

Keywords: memory system, data cache, hybrid memory, non-volatile memory

Procedia PDF Downloads 80

Authors: Huang Bai-Cheng

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When deploying the Oriented Fast and Rotated Binary Robust Independent Elementary Feature (BRIEF) (ORB) extraction algorithm on field-programmable gate arrays (FPGA), the access of global storage for 31×31 pixel patches of the features has become the bottleneck of the system efficiency. Therefore, a feature zone strategy has been proposed. Zones are searched as features are detected. Pixels around the feature zones are extracted from global memory and distributed into patches corresponding to feature coordinates. The proposed FPGA structure is targeted on a Xilinx FPGA development board of Zynq UltraScale+ series, and multiple datasets are tested. Compared with the streaming pixel patch extraction method, the proposed architecture obtains at least two times acceleration consuming extra 3.82% Flip-Flops (FFs) and 7.78% Look-Up Tables (LUTs). Compared with the non-streaming one, the proposed architecture saves 22.3% LUT and 1.82% FF, causing a latency of only 0.2ms and a drop in frame rate for 1. Compared with the related works, the proposed strategy and hardware architecture have the superiority of keeping a balance between FPGA resources and performance.

Keywords: feature extraction, real-time, ORB, FPGA implementation

Procedia PDF Downloads 122

Authors: A. Zarenezhad, A. Esfandiari, E. Zarenezhad, M. Mardkhoshnood

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The aim of this study was to investigate the ultra-structure of the photoreceptor layer of male rats under the effect of methanol intoxication and protective effect of silymarin against the methanol toxicity. Fifteen adult male rats were divided into three groups: Control group, Experimental group I (received 4g/kg methanol by intraperitoneal injection for five days), Experimental group II (received 4 g/kg methanol by intraperitoneal injection for five days and received 250 mg/kg silymarin orally for three months). At the end of the experiment, the eyes were removed; retina was separated near the optic disc and studied by transmission electron microscope. Results showed that the retina in the experimental group I exhibited loss of outer segments and disorganization in inner segment. Increased extra cellular space, disappearance of outer limiting membrane and pyknotic nuclei were seen in this group. But normal outer segment, organized inner segment and normal outer limiting membrane were obvious after treatment with silymarin in experimental group II. These findings show that methanol causes damage in the photoreceptor layer of the rat retina and silymarin can protect the damage to retina against the methanol intoxication.

Keywords: ultra-structure, photoreceptor layer, methanol intoxication, silymarin, rat

Procedia PDF Downloads 293

Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: data mining, digital libraries, digital preservation, file format

Procedia PDF Downloads 499

Authors: Saad A. Bakheet, Salah M. Elkoum, Asharaf A. Almaghribi

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Pre-stressed wire is considered the most important in manufacturing pre-stressed concrete pipes, this is because the life of the mentioned pipes depends on the integrity of the wire. When the wire is wrapped around the concrete core, it provides a compressive strength that enables the concrete cores to withstand internal and external pressures. Since LIBYA has constructed different stages of man-made river projects using pre-stressed concrete pipes in different diameters (1.6, 2.2, 3.6 and 4.0 m) to transport water from the south part of the country to the north-populated area, pipe failures due to corrosion have occurred after several years of operation at different locations of the pipeline route. The PC wire is corroded and, in the end, breaks and becomes unable to withstand internal water pressure. The wire breaks recorded using the hydrophone technique added extra pressure on the project management staff and engineers to resolve and study the possible main cause of wire break and pipe failure. Information regarding the PC wire used will be provided in this paper which includes specifications, manufacturing etc. In this paper, the causes of wire corrosion and wire breaks after several years of PCCP operation will be discussed and explained, in addition to that, the correlation between wire breaks and pipe potential will be addressed and highlighted.

Keywords: wire technical specification, wire break, corrosion causes, potential measurement, failure concrete pipe

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

Procedia PDF Downloads 228

Authors: André C. Silva, Mariana R. Barros, Elenice M. S. Silva, Douglas. Y. Marinho, Diego F. Lopes, Débora N. Sousa, Raphael S. Tomáz

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Modern agriculture requires productivity, efficiency and quality. Therefore, there is need for agricultural limestone implementation that provides adequate amounts of calcium and magnesium carbonates in order to correct soil acidity. During the limestone process, fine particles (with average size under 400#) are generated. These particles do not have economic value in agricultural and metallurgical sectors due their size. When limestone is used for agriculture purposes, these fine particles can be easily transported by wind generated air pollution. Therefore, briquetting, a mineral processing technique, was used to mitigate this problem resulting in an agglomerated product suitable for agriculture use. Briquetting uses compressive pressure to agglomerate fine particles. It can be aided by agglutination agents, allowing adjustments in shape, size and mechanical parameters of the mass. Briquettes can generate extra profits for mineral industry, presenting as a distinct product for agriculture, and can reduce the environmental liabilities of the fine particles storage or disposition. The produced limestone briquettes were subjected to shatter and water action resistance tests. The results show that after six minutes completely submerged in water, the briquettes where fully diluted, a highly favorable result considering its use for soil acidity correction.

Keywords: agglomeration, briquetting, limestone, soil acidity correction

Procedia PDF Downloads 391

Authors: Filip Jaroš

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The Swiss scholar Adolf Portmann (1897-1982) was an outstanding figure in the history of biology and the philosophy of the life sciences. Portmann’s biological theory is primarily focused on the problem of animal form (Gestalt), and it poses a significant counterpart to neo-Darwinian theories about the explanatory primacy of a genetic level over the outer appearance of animals. Besides that, Portmann’s morphological studies related to species-specific ontogeny and the influence of environmental surroundings can be classified as the antecedents of contemporary synthetic approaches such as “eco-evo-devo, “extended synthesis or biosemiotics. The most influential of Portmann’s concepts up to the present is his thesis of a social womb (Soziale Mutterschos): human children are born physiologically premature in comparison with other primates, and they find a second womb in a social environment nurturing their healthy development. It is during the first year of extra-uterine life when a specific human nature is formed, characterized by the strong tie between an individual and a broader historical, cultural whole. In my paper, I will closely analyze: a) the historical coordinates of Portmann’s philosophy of the life sciences (e.g., the philosophical anthropology of A. Gehlen, H. Plessner, and their concept of humans as beings “open to the world”), b) the relation of Portmann’s concept of the social womb to contemporary theories of infant birth evolution.

Keywords: adolf portmann, extended synthesis, philosophical anthropology, social womb

Procedia PDF Downloads 241

Authors: M. Nakkuntod, S. Srinarang, K.W. Hilu

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Water lily (Nymphaea L.) is the largest genus of Nymphaeaceae. This family is composed of six genera (Nuphar, Ondinea, Euryale, Victoria, Barclaya, Nymphaea). Its members are nearly worldwide in tropical and temperate regions. The classification of some species in Nymphaea is ambiguous due to high variation in leaf and flower parts such as leaf margin, stamen appendage. Therefore, the phylogenetic relationships based on 18S rDNA were constructed to delimit this genus. DNAs of 52 specimens belonging to water lily family were extracted using modified conventional method containing cetyltrimethyl ammonium bromide (CTAB). The results showed that the amplified fragment is about 1600 base pairs in size. After analysis, the aligned sequences presented 9.36% for variable characters comprising 2.66% of parsimonious informative sites and 6.70% of singleton sites. Moreover, there are 6 regions of 1-2 base(s) for insertion/deletion. The phylogenetic trees based on maximum parsimony and maximum likelihood with high bootstrap support indicated that genus Nymphaea was a paraphyletic group because of Ondinea, Victoria and Euryale disruption. Within genus Nymphaea, subgenus Nymphaea is a basal lineage group which cooperated with Euryale and Victoria. The other four subgenera, namely Lotos, Hydrocallis, Brachyceras and Anecphya were included the same large clade which Ondinea was placed within Anecphya clade due to geographical sharing.

Keywords: nrDNA, phylogeny, taxonomy, waterlily

Procedia PDF Downloads 143

Authors: Lu Yu Jyun

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Background: Health education is important nursing work aiming to strengthen patients’ self-caring ability and family members. Our department educates through three methods, including speech education, flyer and demonstration video education. The satisfaction rate of health education tool use is 54.3% in nursing staff. The main reason is there hadn’t been a storage area for flyers, causing extra workload in assessing flyers. The satisfaction rate of health education in patients and families is 70.7%. We aim to improve this situation between 13th April and 6th June 2021. Method: We introduce the ECRS method to erase repetitive and redundant actions. We redesign the health education tool usage workflow to improve nursing staffs’ efficiency and further enhance nursing staffs care quality and working satisfaction. Result: The satisfaction rate of health education tool usage in nursing staff elevated from 54.3% to 92.5%. The satisfaction rate of health education in patients and families elevated from 70.7% to 90.2%. Conclusion: The assessment time of health care tools dropped from 10minutes to 3minutes. This significantly reduced the nursing staffs’ workload. 1213 paper is saved in one month and 14,556 a year in the estimate; we save the environment via this action. Health education map implemented in other nursing departments since October due to its’ high efficiency and makes health care tools more humanize.

Keywords: health, education tools, satisfaction, nursing staff

Procedia PDF Downloads 150

Authors: Ria Forner

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Introduction: Due to increases in the SNR at 7T and above, it becomes more favourable to make use of X-nuclear imaging. Integrated body coils tuned to 120MHz for 31P, 79MHz for 23Na, and 75 MHz for 13C at 7T were simulated with a human male, female, or child body model to assess strategies of use for metabolic MR imaging in the body. Methods: B1 and SAR efficiencies in the heart, liver, spleen, and kidneys were assessed using numerical simulations over the three frequencies with phase shimming. Results: B1+ efficiency is highly variable over the different organs, particularly for the highest frequency; however, local SAR efficiency remains relatively constant over the frequencies in all subjects. Although the optimal phase settings vary, one generic phase setting can be identified for each frequency at which the penalty in B1+ is at a max of 10%. Discussion: The simulations provide practical strategies for power optimization, B1 management, and maintaining safety. As expected, the B1 field is similar at 75MHz and 79MHz, but reduced at 120MHz. However, the B1 remains relatively constant when normalised by the square root of the peak local SAR. This is in contradiction to generalized SAR considerations of 1H MRI at different field strengths, which is defined by global SAR instead. Conclusion: Although the B1 decreases with frequency, SAR efficiency remains constant throughout the investigated frequency range. It is possible to shim the body coil to obtain a maximum of 10% extra B1+ in a specific organ in a body when compared to a generic setting.

Keywords: birdcage, multi-nuclear, B1 shimming, 7 Tesla MRI, liver, kidneys, heart, spleen

Procedia PDF Downloads 67

Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

Procedia PDF Downloads 501

Authors: Newroz Nooralddin Abdulrazaq, Thuraya Mahmood Qaradaghi

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The McEliece cryptosystem is an asymmetric type of cryptography based on error correction code. The classical McEliece used irreducible binary Goppa code which considered unbreakable until now especially with parameter [1024, 524, and 101], but it is suffering from large public key matrix which leads to be difficult to be used practically. In this work Irreducible and Separable Goppa codes have been introduced. The Irreducible and Separable Goppa codes used are with flexible parameters and dynamic error vectors. A Comparison between Separable and Irreducible Goppa code in McEliece Cryptosystem has been done. For encryption stage, to get better result for comparison, two types of testing have been chosen; in the first one the random message is constant while the parameters of Goppa code have been changed. But for the second test, the parameters of Goppa code are constant (m=8 and t=10) while the random message have been changed. The results show that the time needed to calculate parity check matrix in separable are higher than the one for irreducible McEliece cryptosystem, which is considered expected results due to calculate extra parity check matrix in decryption process for g2(z) in separable type, and the time needed to execute error locator in decryption stage in separable type is better than the time needed to calculate it in irreducible type. The proposed implementation has been done by Visual studio C#.

Keywords: McEliece cryptosystem, Goppa code, separable, irreducible

Procedia PDF Downloads 267

Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

Procedia PDF Downloads 133

Authors: Dong Jin Kang

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Application of single board microcontrollers is an important skill even for non-electronic engineering major students. Arduino board is widely utilized in engineering classes of the Yeungnam University of South Korea. In those classes, students are subjected to learn how to use various sensor components related to motion, sound, light, and so on as well as physical quantities. Students are grouped into several teams, and each team consists of 4~5 students. Many students are not motivated enough to learn those skills. An extracurricular program was planned to improve this problem. The extracurricular program was held as an international boot camp where students from three different countries were invited to participate. 10 students groups were formed, and each team was consisted of students having different nationality. The camp was 4 days long and wrapped up with competitions. During the camp, every student was assigned to design and make a two wheel robot. The competition was carried out in two different areas; individual and group performances. As most skills dealt in the class are used to build the robot, students are much motivated to review the whole subjects of the class. All students were surveyed after the program. The survey shows that the skills studied in the class are greatly improved, and practically understood. Staying at the dormitory and teaming with international students are help students improve communication skills. Competition at the camp was found as a key element to inspire and attract students for voluntary participation.

Keywords: extracurricular program, robot, Arduino board, international camp, competition

Procedia PDF Downloads 219

Authors: Masood Ahmed

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There is a plethora of literature (including judicial and extra-judicial comments) concerning the virtues of alternative dispute resolution processes within the English civil justice system. Lord Woolf in his Access to Justice Report ushered in a new pro-ADR philosophy and this was reinforced by Sir Rupert Jackson in his review of civil litigation costs. More recently, Briggs LJ, in his review of the Chancery Court, reiterated the significant role played by ADR and the need for better integration of ADR processes within the Chancery Court. His Lordship also noted that ADR which had failed to produce a settlement (i.e. a failed-ADR) continued to play a significant role in contributing to a ‘substantial narrowing of the issues or increased focus on the key issues’ which were ‘capable of assisting both the parties and the court in the economical determination of the dispute at trial.’ With the assistance of empirical data, this paper investigates the nature of failed-ADR and, in particular, assesses the effectiveness of failed-ADR processes as a tool in: (a) narrowing the legal and/or factual issues which may assist the courts in more effective and efficient case management of the dispute; (b) assisting the parties in the future settlement of the matter. This paper will also measure the effectiveness of failed-ADR by considering the views and experiences of legal practitioners who have engaged in failed-ADR.

Keywords: English civil justice system, alternative dispute resolution processes, civil court process, empirical data from legal profession regarding failed ADR

Procedia PDF Downloads 466

Authors: Kitda Praraththajariya

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The main purpose of this research was to study how to communicate the identity of the Mueang district, Samut Songkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: 1. The identity of Amphur (District) Mueang, Samut Songkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. 2. The communication of the identity of Amphur Mueang, Samut Songkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of Amphur Mueang, Samut Songkram province 2) Wat Phet Samut Worrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep Amphur Mueang, Samut Songkram province for ecotourism.

Keywords: foreigner tourists, signified, semiotics, ecotourism

Procedia PDF Downloads 240

Authors: Khin Mar Yee, Mu Mu Than, Kyi Lint, Aye Aye Oo, Chan Mya Hmway, Khin Zar Chi Winn

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The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping.

Keywords: land use and land cover change, change detection, image processing, support vector machines

Procedia PDF Downloads 140

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

Procedia PDF Downloads 45

Authors: Mengyu Lu, Yongsheng Guo

Abstract:

This study investigates how employee incentives affect employee performance in financing from customers. This study followed a grounded theory approach where data were collected through 29 interviews. Main themes and categories were identified through the coding processes. This study found that casual conditions, including financial barriers, informal finance, business location, customer base and customer relationship, influenced the adoption of customer finance in the case of SMEs. The SMEs build and maintain long-term relationships with customers through personal communications. The SMEs engage and motivate employees in customer communications and business financing strategy through financial incentives programs, including bonuses, salary rises, rewards and non-financial incentives, including training opportunities, extra holiday leave, and flexible working hours. Employee performance was measured through financing contribution and job contribution. As a consequence, customers will be well served by employees and get a better customer experience. SMEs can get benefits such as employee engagement, employee satisfaction and sustainable financing sources. This study gets in sight of employee incentives in improving employee performance in customer finance and makes implications to human capital theories. Suggestions are provided to the decision-makers in businesses as incentive programs improve employee performance that, eventually contributes to overall business performance.

Keywords: SMEs, financing from customers, employee incentives, performance-based measurement

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Authors: Roven Tumaneng, Angelica Kristina Monzon, Ralph Sedricke Lapuz, Jose Don De Alban, Jennica Paula Masigan, Joanne Rae Pales, Laila Monera Pornel, Dennis Tablazon, Rizza Karen Veridiano, Jackie Lou Wenceslao, Edmund Leo Rico, Neil Aldrin Mallari

Abstract:

Most protected area management plans in the Philippines, particularly the smaller and more remote islands suffer from insufficient baseline data, which should provide the bases for formulating measureable conservation targets and appropriate management interventions for these protected areas. Attempts to synthesize available data particularly on cultural and socio-economic characteristic of local peoples within and outside protected areas also suffer from the lack of comprehensive and detailed inventories, which should be considered in designing adaptive management interventions to be used for those protected areas. Mt Guiting-guiting Natural Park (MGGNP) located in Sibuyan Island is one of the poorly studied protected areas in the Philippines. In this study, we determined the highly biologically important areas of the protected area using Maximum Entropy approach (MaxEnt) from environmental predictors (i.e., topographic, bioclimatic,land cover, and soil image layers) derived from global remotely sensed data and point occurrence data of species of birds and trees recorded during field surveys on the island. A total of 23 trigger species of birds and trees was modeled and stacked to generate species richness maps for biological high conservation value areas (HCVAs). Forest habitat change was delineated using dual-polarised L-band ALOS-PALSAR mosaic data at 25 meter spatial resolution, taken at two acquisition years 2007 and 2009 to provide information on forest cover ad habitat change in the island between year 2007 and 2009. Determining the livelihood guilds were also conducted using the data gathered from171 household interviews, from which demographic and livelihood variables were extracted (i.e., age, gender, number of household members, educational attainment, years of residency, distance from forest edge, main occupation, alternative sources of food and resources during scarcity months, and sources of these alternative resources).Using Principal Component Analysis (PCA) and Kruskal-Wallis test, the diversity and patterns of forest resource use by people in the island were determined with particular focus on the economic activities that directly and indirectly affect the population of key species as well as to identify levels of forest resource use by people in different areas of the park.Results showed that there are gaps in the area occupied by the natural park, as evidenced by the mismatch of the proposed HCVAs and the existing perimeters of the park. We found out that subsistence forest gathering was the possible main driver for forest degradation out of the eight livelihood guilds that were identified in the park. Determining the high conservation areas and identifyingthe anthropogenic factors that influence the species richness and abundance of key species in the different management zone of MGGNP would provide guidance for the design of a protected area management plan and future monitoring programs. However, through intensive communication and consultation with government stakeholders and local communities our results led to setting conservation targets in local development plans and serve as a basis for the reposition of the boundaries and reconfiguration of the management zones of MGGNP.

Keywords: conservation gaps, livelihood guilds, MaxEnt, protected area

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Authors: Susana Olmos, Catherine Spencer

Abstract:

Every year our School in DIT (Dublin Institute of Technology) hosts approximately 80 Erasmus students from partner universities across Europe. Our own students are required to spend a compulsory 3rd year abroad on study and/or work placements. This is an extremely rewarding experience for all of the students, however, it can also be a challenging one. With this in mind, we started a project which aimed to make this transition as easy and productive as possible. The project, which is called MEET: Maximise the Erasmus Experience Together, focuses on the students’ own active engagement in learning and preparation – outside of the classroom –and their own self-directed pursuit of opportunities to develop their confidence and preparedness, which would work as an important foundation for the transformative learning that study abroad implies. We focussed on creating more structured opportunities where Erasmus students from our partner universities (currently studying at DIT) and our second-year students could interact and learn from each other, and in so doing improve both their language and intercultural skills. Our experience so far has been quite positive and we have seen how students taking part in this project have developed as autonomous learners as well as enhanced both their linguistic and intercultural knowledge. As the linguistic element of our project was one of our main priorities, we asked the students to keep a reflective diary on the activities that were organised by the group in the TL. Also, we use questionnaires as well as personal interviews to assess their development. However, there are challenges and proposals we would make to bring this project forward for the near future.

Keywords: erasmus, intercultural competence, linguistic competence, extra curriculum activities

Procedia PDF Downloads 378

Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

Abstract:

Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

Abstract:

This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: May M. Youssef

Abstract:

This article introduces a design, for additive manufacturing technology, face shield as Personal Protective Equipment from the respiratory viruses such as coronavirus 2. The face shields help to reduce ocular exposure and play a vital role in diverting away from the respiratory COVID-19 air droplets around the users' face. The proposed face shield comprises three assembled polymer parts. The frame with a transparency overhead projector sheet visor is suitable for frontline health care workers and ordinary citizens. The frame design allows tightening the shield around the user’s head and permits rubber elastic straps to be used if required. That ergonomically designed with a unique face mask support used in case of wearing extra protective mask was created using computer aided design (CAD) software package. The finite element analysis (FEA) structural verification of the proposed design is performed by an advanced simulation technique. Subsequently, the prototype model was fabricated by a 3D printing using Fused Deposition Modeling (FDM) as a globally developed face shield product. This study provides a different face shield designs for global production, which showed to be suitable and effective toward supply chain shortages and frequent needs of personal protective goods during coronavirus disease and similar viruses.

Keywords: additive manufacturing, Coronavirus-19, face shield, personal protective equipment, 3D printing

Procedia PDF Downloads 205