Search results for: four-tier classification system

Authors: R. A. Deshpande, P. Chandhra Sekhar, V. Sankar

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In this paper, we discussed the basic reliability evaluation techniques needed to evaluate the reliability of distribution systems which are applied in distribution system planning and operation. Basically, the reliability study can also help to predict the reliability performance of the system after quantifying the impact of adding new components to the system. The number and locations of new components needed to improve the reliability indices to certain limits are identified and studied.

Keywords: distribution system, reliability indices, urban feeder, rural feeder

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Authors: Zheng Sun, Weiwei Cui, Xiaodong Ma, Hongxin Jin, Dongpao Hong, Jinsong Yang, Jingyi Sun

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With the improvement of the performance and complexity of modern equipment, automatic test system (ATS) becomes widely used for condition monitoring and fault diagnosis. However, the conventional ATS mainly works in a serial mode, and lacks the ability of testing several equipments at the same time. That leads to low test efficiency and ATS redundancy. Especially for a large majority of equipment under test, the conventional ATS cannot meet the requirement of efficient testing. To reduce the support resource and increase test efficiency, we propose a method of design for the parallel test based on the embedded system in this paper. Firstly, we put forward the general framework of the parallel test system, and the system contains a central management system (CMS) and several distributed test subsystems (DTS). Then we give a detailed design of the system. For the hardware of the system, we use embedded architecture to design DTS. For the software of the system, we use test program set to improve the test adaption. By deploying the parallel test system, the time to test five devices is now equal to the time to test one device in the past. Compared with the conventional test system, the proposed test system reduces the size and improves testing efficiency. This is of great significance for equipment to be put into operation swiftly. Finally, we take an industrial control system as an example to verify the effectiveness of the proposed method. The result shows that the method is reasonable, and the efficiency is improved up to 500%.

Keywords: parallel test, embedded system, automatic test system, automatic test system (ATS), central management system, central management system (CMS), distributed test subsystems, distributed test subsystems (DTS)

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Authors: R. Rezaeipour, A. R. Mehrabi

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Recent increase of the DGs and microgrids in distribution systems, disturbs the tradition structure of the system. Coordination between protection devices in such a system becomes the concern of the network operators. This paper presents a new method for fault location detection in the active distribution networks, independent of the fault type or its resistance. The method uses synchronized voltage and current measurements at the interconnection of DG units and is able to adapt to changes in the topology of the system. The method has been tested on a 38-bus distribution system, with very encouraging results.

Keywords: fault location detection, active distribution system, micro grids, network operators

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Authors: Ow Tze Weng, Suhaila Isaak, Yusmeeraz Yusof

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The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG sensors are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a high performance CMOS amplifier for ECG sensors that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV/√Hz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC.

Keywords: CMOS, ECG, amplifier, low power

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Authors: Gianne Bianca P. Manalo, Ernesto A. Martin, Vanessa V. Velasco

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The laying performance, egg quality, egg classification, and income over feed cost of Improved Philippine Mallard duck (Itik Pinas) were examined as influenced by garlic powder in drinking water. A total of 48 ducks (42 females and 6 males) were used in the study. The ducks were allocated into two treatments - with garlic powder (GP) and without garlic powder (control) in drinking water. Each treatment had three replicates with eight ducks (7 females and 1 male) per replication. The results showed that there was a significant (P = 0.03) difference in average egg weight where higher values were attained by ducks with GP (77.67 g ± 0.64) than the control (75.64 g ± 0.43). The supplementation of garlic powder in drinking water, however, did not affect the egg production, feed intake, FCR, egg mass, livability, egg quality and egg classification. The Itik Pinas with GP in drinking water had numerically higher income over feed cost than those without. GP in drinking water can be considered in raising Itik Pinas. Further studies on increasing level of GP and long feeding duration also merit consideration to substantiate the findings.

Keywords: phytogenic, garlic powder, Itik-Pinas, egg weight, egg production

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Authors: Zhou Yingkun, Xu Guorui, Wei Siming, Huang Yongzhang

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With the increasing of the penetration of renewable energy in power system, the whole inertia of the power system is declining, which will endanger the frequency stability of the power system. In order to enhance the inertia, virtual synchronous generator (VSG) has been proposed. In addition, the motor-generator pair (MGP) system is proposed to enhance grid inertia. Both of them need additional equipment to provide instantaneous energy, so the economic problem should be considered. In this paper, the basic working principle of MGP system and VSG are introduced firstly. Then, the technical characteristics and economic investment of MGP/VSG are compared by calculation and simulation. The results show that the MGP system can provide same inertia with less cost than VSG.

Keywords: high renewable energy penetration, inertia of power system, motor-generator pair (MGP) system, virtual synchronous generator (VSG), techno-economic analysis

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Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Reeden Bicomong

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Ecotourism activities, though geared towards conservation efforts, still put pressures against the natural state of the environment. Influx of visitors that goes beyond carrying capacity of the ecotourism site, the wastes generated, greenhouse gas emissions, are just few of the potential negative impacts of a not well-managed ecotourism activities. According to Girard and Nocca (2017) tourism produces many negative impacts because it is configured according to the model of linear economy, operating on a linear model of take, make and dispose (Ellen MacArthur Foundation 2015). With the influx of tourists in an ecotourism area, more wastes are generated, and if unregulated, natural state of the environment will be at risk. It is in this light that a study on waste analysis and classification study in five different ecotourism sites of Samal Island, Philippines was conducted. The major objective of the study was to analyze the amount and content of wastes generated from ecotourism sites in Samal Island, Philippines and make recommendations based on the circular economy perspective. Five ecotourism sites in Samal Island, Philippines was identified such as Hagimit Falls, Sanipaan Vanishing Shoal, Taklobo Giant Clams, Monfort Bat Cave, and Tagbaobo Community Based Ecotourism. Ocular inspection of each ecotourism site was conducted. Likewise, key informant interview of ecotourism operators and staff was done. Wastes generated from these ecotourism sites were analyzed and characterized to come up with recommendations that are based on the concept of circular economy. Wastes generated were classified into biodegradables, recyclables, residuals and special wastes. Regression analysis was conducted to determine if increase in number of visitors would equate to increase in the amount of wastes generated. Ocular inspection indicated that all of the five ecotourism sites have their own system of waste collection. All of the sites inspected were found to be conducting waste separation at source since there are different types of garbage bins for all of the four classification of wastes such as biodegradables, recyclables, residuals and special wastes. Furthermore, all five ecotourism sites practice composting of biodegradable wastes and recycling of recyclables. Therefore, only residuals are being collected by the municipal waste collectors. Key informant interview revealed that all five ecotourism sites offer mostly nature based activities such as swimming, diving, site seeing, bat watching, rice farming experiences and community living. Among the five ecotourism sites, Sanipaan Vanishing Shoal has the highest average number of visitors in a weekly basis. At the same time, in the wastes assessment study conducted, Sanipaan has the highest amount of wastes generated. Further results of wastes analysis revealed that biodegradables constitute majority of the wastes generated in all of the five selected ecotourism sites. Meanwhile, special wastes proved to be the least generated as there was no amount of this type was observed during the three consecutive weeks WACS was conducted.

Keywords: Circular economy, ecotourism, sustainable development, WACS

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Authors: Anocha Rojanapanich

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The purpose of this research is to develop and design an accounting management information system for convenient shop in Bangkok Thailand. The study applied the System Development Life Cycle (SDLC) for development which began with study and analysis of current data, including the existing system. Then, the system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Product diversity, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management and importance of cost information for decision making also as well as.

Keywords: accounting management information system, convenient shop, cost information for decision making system, development life cycle

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: Duc V. Nguyen

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Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system.

Keywords: fault detection, FFT, induction motor, predictive maintenance

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: Ülle Parm, Kata Pedamäe, Jaak Jürimäe, Evelin Lätt, Aivar Orav, Anna-Liisa Tamm

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Vegetarianism has gained popularity across the world. It`s being chosen for multiple reasons, but among Estonians, these have remained unknown. Previously, attention to bone health and probable nutrient deficiency of vegetarians has been paid and in vegetarians lower body mass index (BMI) and blood cholesterol level has been found but the results are inconclusive. The goal was to explain reasons for choosing vegetarian diet in Estonia and impact of vegetarianism to body composition – BMI, fat percentage (fat%), fat mass (FM), and fat free mass (FFM). The study group comprised of 68 vegetarians and 103 omnivorous. The determining body composition with DXA (Hologic) was concluded in 2013. Body mass (medical electronic scale, A&D Instruments, Abingdon, UK) and height (Martin metal anthropometer to the nearest 0.1 cm) were measured and BMI calculated (kg/m2). General data (physical activity level included) was collected with questionnaires. The main reasons why vegetarianism was chosen were the healthiness of the vegetarian diet (59%) and the wish to fight for animal rights (72%) Food additives were consumed by less than half of vegetarians, more often by men. Vegetarians had lower BMI than omnivores, especially amongst men. Based on BMI classification, vegetarians were less obese than omnivores. However, there were no differences in the FM, FFM and fat percentage figures of the two groups. Higher BMI might be the cause of higher physical activity level among omnivores compared with vegetarians. For classifying people as underweight, normal weight, overweight and obese both BMI and fat% criteria were used. By BMI classification in comparison with fat%, more people in the normal weight group were considered; by using fat% in comparison with BMI classification, however, more people categorized as overweight. It can be concluded that the main reasons for vegetarianism chosen in Estonia are healthiness of the vegetarian diet and the wish to fight for animal rights and vegetarian diet has no effect on body fat percentage, FM and FFM.

Keywords: body composition, body fat percentage, body mass index, vegetarianism

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Kisik Song, Sungjoo Lee

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With the growing importance of intangible assets recently, the impact of patent infringement on the business of a company has become more evident. Accordingly, it is essential for firms to estimate the risk of patent infringement risk before developing a technology and create new technology ideas to avoid the risk. Recognizing the needs, several attempts have been made to help develop new technology opportunities and most of them have focused on identifying emerging vacant technologies from patent analysis. In these studies, the IPC (International Patent Classification) system or keywords from text-mining application to patent documents was generally used to define vacant technologies. Unlike those studies, this study adopted F-term, which classifies patent documents according to the technical features of the inventions described in them. Since the technical features are analyzed by various perspectives by F-term, F-term provides more detailed information about technologies compared to IPC while more systematic information compared to keywords. Therefore, if well utilized, it can be a useful guideline to create a new technology idea. Recognizing the potential of F-term, this paper aims to suggest a novel approach to developing new technology ideas to avoid patent infringement based on F-term. For this purpose, we firstly collected data about F-term and then applied text-mining to the descriptions about classification criteria and attributes. From the text-mining results, we could identify other technologies with similar technical features of the existing one, the patented technology. Finally, we compare the technologies and extract the technical features that are commonly used in other technologies but have not been used in the existing one. These features are presented in terms of “purpose”, “function”, “structure”, “material”, “method”, “processing and operation procedure” and “control means” and so are useful for creating new technology ideas that help avoid infringing patent rights of other companies. Theoretically, this is one of the earliest attempts to adopt F-term to patent analysis; the proposed methodology can show how to best take advantage of F-term with the wealth of technical information. In practice, the proposed methodology can be valuable in the ideation process for successful product and service innovation without infringing the patents of other companies.

Keywords: patent infringement, new technology ideas, patent analysis, F-term

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Authors: Ossama Matrane

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Information System has become a strategic lever for enterprises. It contributes effectively to align business processes on strategies of enterprises. It is regarded as an increase in productivity and effectiveness. So, many organizations are currently involved in implementing sustainable Information System. And, a large number of studies have been conducted the last decade in order to define the success factors of information system. Thus, many studies on maturity model have been carried out. Some of this study is referred to the maturity model of Information System. In this article, we report on development of maturity models specifically designed for information system. This model is built based on three components derived from Maturity Model for Information Security Management, OPM3 for Project Management Maturity Model and processes of COBIT for IT governance. Thus, our proposed model defines three maturity stages for corporate a strong Information System to support objectives of organizations. It provides a very practical structure with which to assess and improve Information System Implementation.

Keywords: information system, maturity models, information security management, OPM3, IT governance

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Saiakhil Chilaka

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Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that supervision risk score, percent days employed, and education level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliation emerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners.

Keywords: juvenile, justice system, data analysis, SHAP

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Authors: Khawlah Al-Khalidi

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Prevalence of lower third molar impactions and angulations among Yemeni population The purpose of this study was to look into the prevalence of lower third molars in a sample of patients from Ibb University Affiliated Hospital, as well as to study and categorise their position by using Pell and Gregory classification, and to look into a possible correlation between their position and the indication for extraction. Materials and methods: This is a retrospective, observational study in which a sample of 200 patients from Ibb University Affiliated Hospital were studied, including patient record validation and orthopantomography performed in screening appointments in people aged 16 to 21. Results and discussion: Males make up 63% of the sample, while people aged 19 to 20 make up 41.2%. Lower third molars were found in 365 of the 365 instances examined, accounting for 91% of the sample under study. According to Pell and Gregory's categorisation, the most common position is IIB, with 37%, followed by IIA with 21%; less common classes are IIIA, IC, and IIIC, with 1%, 3%, and 3%, respectively. It was feasible to determine that 56% of the lower third molars in the sample were recommended for extraction during the screening consultation. Finally, there are differences in third molar location and angulation. There was, however, a link between the available space for third molar eruption and the need for tooth extraction.

Keywords: lower third molar, extraction, Pell and Gregory classification, lower third molar impaction

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Karim Akkouche

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Many methods have been developed for characterizing the behavior of bolted joints. However, in the presence of a certain model of stiffeners, no orientation was given in relation to their modeling. To this end, multitude of coarse errors can arise in the reproduction of the propagation of efforts and in representation of the modes of deformations. Considering these particularities, a numerical investigation was carried out in our laboratory. In this paper we will present a comparative study between three types of assemblies. A non-linear 3D modeling was chosen, given that it takes into consideration geometric and material non-linearity, using the Finite Element calculation code ABAQUS. Initially, we evaluated the influence of the presence of each stiffener on the "global" behavior of the assemblies, this by analyzing their Moment-Rotation curves, also by referring to the classification system proposed by NF EN 1993- 1.8 which is based on the resisting moment Mj-Rd and the initial stiffness Sj.int. In a second step, we evaluated the "local" behavior of their components by referring to the stress-strain curves.

Keywords: assembly, post-beam, end plate, nonlinearity

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Authors: Naman Solanki, Souraj Mondal

Abstract:

With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Gwanghee Heo, Geonhyeok Bang, Chunggil Kim, Chinok Lee

Abstract:

This paper attempts to develop a wireless feedback control system as a primary step eventually toward a bio-inspired structure system where inanimate structure behaves like a life form autonomously. It is a standalone wireless control system which is supposed to measure externally caused structural responses, analyze structural state from acquired data, and take its own action on the basis of the analysis with an embedded logic. For an experimental examination of its effectiveness, we applied it on a model of two-span bridge and performed a wireless control test. Experimental tests have been conducted for comparison on both the wireless and the wired system under the conditions of Un-control, Passive-off, Passive-on, and Lyapunov control algorithm. By proving the congruence of the test result of the wireless feedback control system with the wired control system, its control performance was proven to be effective. Besides, it was found to be economical in energy consumption and also autonomous by means of a command algorithm embedded into it, which proves its basic capacity as a bio-inspired system.

Keywords: structural vibration control, wireless system, MR damper, feedback control, embedded system

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Authors: Lela Gadrani, Mariam Tsitsagi

Abstract:

Human is a part of the urban landscape and responsible for it. Urbanization of cities includes the longest phase; thus none of the environment ever undergoes such anthropogenic impact as the area of large cities. The post-Soviet period is very interesting in terms of scientific research. The changes that have occurred in the cities since the collapse of the Soviet Union have not yet been analyzed best to our knowledge. In this context, the aim of this paper is to analyze the changes in the land use of the three large cities of Georgia (Tbilisi, Kutaisi, Batumi). Tbilisi as a capital city, Batumi as a port city, and Kutaisi as a former industrial center. Data used during the research process are conventionally divided into satellite and supporting materials. For this purpose, the largest topographic maps (1:10 000) of all three cities were analyzed, Tbilisi General Plans (1896, 1924), Tbilisi and Kutaisi historical maps. The main emphasis was placed on the classification of Landsat images. In this case, we have classified the images LULC (LandUse / LandCover) of all three cities taken in 1987 and 2016 using the supervised and unsupervised methods. All the procedures were performed in the programs: Arc GIS 10.3.1 and ENVI 5.0. In each classification we have singled out the following classes: built-up area, water bodies, agricultural lands, green cover and bare soil, and calculated the areas occupied by them. In order to check the validity of the obtained results, additionally we used the higher resolution images of CORONA and Sentinel. Ultimately we identified the changes that took place in the land use in the post-Soviet period in the above cities. According to the results, a large wave of changes touched Tbilisi and Batumi, though in different periods. It turned out that in the case of Tbilisi, the area of developed territory has increased by 13.9% compared to the 1987 data, which is certainly happening at the expense of agricultural land and green cover, in particular, the area of agricultural lands has decreased by 4.97%; and the green cover by 5.67%. It should be noted that Batumi has obviously overtaken the country's capital in terms of development. With the unaided eye it is clear that in comparison with other regions of Georgia, everything is different in Batumi. In fact, Batumi is an unofficial summer capital of Georgia. Undoubtedly, Batumi’s development is very important both in economic and social terms. However, there is a danger that in the uneven conditions of urban development, we will eventually get a developed center - Batumi, and multiple underdeveloped peripheries around it. Analysis of the changes in the land use is of utmost importance not only for quantitative evaluation of the changes already implemented, but for future modeling and prognosis of urban development. Raster data containing the classes of land use is an integral part of the city's prognostic models.

Keywords: analysis, geo information system, remote sensing, LULC

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Authors: Shafiq ur Rehman, Zubair Ahmed Shaikh

Abstract:

This research provides the comparative study of different mobile payment system and proposes an efficient solution of mobile payment system. The research involves discovering how the mobile payment methods can be used and implemented keeping user and system interaction under consideration. The implementation of Nielsen’s heuristic and universal design principles enhanced the user’s interaction design and made the system more appropriate, understandable and visible to the end user. The design of application is greatly affected by the user driven factors. These factors help in the efficiency of the application usage.

Keywords: mobile payment system, m-commerce, usability, near field communication

Procedia PDF Downloads 452

Authors: Satien Janpla, Thanawan Boonpuck, Pattarapan Roonrakwit

Abstract:

In this paper, we present a hamster knowledge system based on android application. The objective of this system is to advice user to upkeep and feed hamsters based on mobile application. We describe the design approaches and functional components of this system. The system was developed based on knowledge based of hamster experts. The results were divided by the research purposes into 2 parts: developing the mobile application for advice users and testing and evaluating the system. Black box technique was used to evaluate application performances and questionnaires were applied to measure user satisfaction with system usability by specialists and users.

Keywords: hamster knowledge, Android application, black box, questionnaires

Procedia PDF Downloads 341