Search results for: encrypted traffic classification

Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

Abstract:

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

Procedia PDF Downloads 446

Authors: Nikita Katyal, Shaurya Uppal

Abstract:

This paper tackles the significant challenge of accurately classifying customers within a retailer’s customer base. Accurate classification is essential for developing targeted marketing strategies that effectively engage this important demographic. To address this issue, we propose a method that utilizes Large Language Models (LLMs). By employing LLMs, we analyze the metadata associated with product purchases derived from historical data to identify key product categories that act as distinguishing factors. These categories, such as baby food, eldercare products, or family-sized packages, offer valuable insights into the likely household composition of customers, including families with babies, families with kids/teenagers, families with pets, households caring for elders, or mixed households. We segment high-confidence customers into distinct categories by integrating historical purchase behavior with LLM-powered product classification. This paper asserts that life stage segmentation can significantly enhance e-commerce businesses’ ability to target the appropriate customers with tailored products and campaigns, thereby augmenting sales and improving customer retention. Additionally, the paper details the data sources, model architecture, and evaluation metrics employed for the segmentation task.

Keywords: LLMs, segmentation, product tags, fine-tuning, target segments, marketing communication

Procedia PDF Downloads 28

Authors: Yeliz Karaca, Carlo Cattani

Abstract:

This study focuses on the local Holder exponent as a measure of the function regularity for time series related to finance data. In this study, the attributes of the finance dataset belonging to 13 countries (India, China, Japan, Sweden, France, Germany, Italy, Australia, Mexico, United Kingdom, Argentina, Brazil, USA) located in 5 different continents (Asia, Europe, Australia, North America and South America) have been examined.These countries are the ones mostly affected by the attributes with regard to financial development, covering a period from 2012 to 2017. Our study is concerned with the most important attributes that have impact on the development of finance for the countries identified. Our method is comprised of the following stages: (a) among the multi fractal methods and Brownian motion Holder regularity functions (polynomial, exponential), significant and self-similar attributes have been identified (b) The significant and self-similar attributes have been applied to the Artificial Neuronal Network (ANN) algorithms (Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) (c) the outcomes of classification accuracy have been compared concerning the attributes that have impact on the attributes which affect the countries’ financial development. This study has enabled to reveal, through the application of ANN algorithms, how the most significant attributes are identified within the relevant dataset via the Holder functions (polynomial and exponential function).

Keywords: artificial neural networks, finance data, Holder regularity, multifractals

Procedia PDF Downloads 247

Authors: Sushma Ghogale

Abstract:

With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

Procedia PDF Downloads 98

Authors: Zegrar Ahmed, Ghabi Mohamed

Abstract:

The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

Procedia PDF Downloads 340

Authors: Kwaku Damoah

Abstract:

This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

Procedia PDF Downloads 71

Authors: Moh’d A. AL-Shboul

Abstract:

Recently, and due to Arab spring and terrorism around the globe, pushing and driving most governments potentially to harmonize their border measures particularly the regional and an international transit trade within and among Customs Unions. The main purpose of this study is to investigate and provide an insight for monitoring and controlling the trade supply chain within and among different countries by using technological advancement (i.e. an electronic tracking system, etc.); furthermore, facilitate the local and intra-regional trade among countries through reviewing the recent trends and practical implementation of an electronic transit traffic and cargo that related to customs measures by introducing and supporting some case studies of several international and landlocked transit trade countries. The research methodology employed in this study was described as qualitative by conducting few interviews with managers, transit truck drivers, and traders and reviewing the related literature to collect qualitative data from secondary sources such as statistical reports, previous studies, etc. The results in this study show that Jordan and other countries around the globe that used an electronic tracking system for monitoring transit trade has led to a significant reduction in cost, effort and time in physical movement of goods internally and crossing through other countries. Therefore, there is no need to escort transit trucks by customs staff; hence, the rate of escort transit trucks is reduced by more than ninety percent, except the bulky and high duty goods. Electronic transit traffic has been increased; the average transit time journey has been reduced by more than seventy percent and has led to decrease in rates of smuggling up to fifty percent. The researcher recommends considering Jordan as regional and international office for tracking electronically and monitoring the transit trade for many considerations.

Keywords: electronic tracking system, facilitation system, regional and international corridor, supply chain security, transit trade

Procedia PDF Downloads 504

Authors: Anitha Muralidhara, Victor Engelen, Christophe Len, Pascal Pandard, Guy Marlair

Abstract:

Production of bio-based chemicals and materials from lignocellulosic biomass is gaining tremendous importance in advanced bio-refineries while aiming towards progressive replacement of petroleum based chemicals in transportation fuels and commodity polymers. One such attempt has resulted in the production of key furan derivatives (FD) such as furfural, HMF, MMF etc., via acid catalyzed dehydration (ACD) of C6 and C5 sugars, which are further converted into key chemicals or intermediates (such as Furandicarboxylic acid, Furfuryl alcohol etc.,). In subsequent processes, many high potential FD are produced, that can be converted into high added value polymers or high energy density biofuels. During ACD, an unavoidable polyfuranic byproduct is generated which is called humins. The family of FD is very large with varying chemical structures and diverse physicochemical properties. Accordingly, the associated risk profiles may largely vary. Hazardous Material (Haz-mat) classification systems such as GHS (CLP in the EU) and the UN TDG Model Regulations for transport of dangerous goods are one of the preliminary requirements for all chemicals for their appropriate classification, labelling, packaging, safe storage, and transportation. Considering the growing application routes of FD, it becomes important to notice the limited access to safety related information (safety data sheets available only for famous compounds such as HMF, furfural etc.,) in these internationally recognized haz-mat classification systems. However, these classifications do not necessarily provide information about the extent of risk involved when the chemical is used in any specific application. Factors such as thermal stability, speed of combustion, chemical incompatibilities, etc., can equally influence the safety profile of a compound, that are clearly out of the scope of any haz-mat classification system. Irrespective of the bio-based origin, FD has so far received inconsistent remarks concerning their toxicity profiles. With such inconsistencies, there is a fear that, a large family of FD may also follow extreme judgmental scenarios like ionic liquids, by ranking some compounds as extremely thermally stable, non-flammable, etc., Unless clarified, these messages could lead to misleading judgements while ranking the chemical based on its hazard rating. Safety is a key aspect in any sustainable biorefinery operation/facility, which is often underscored or neglected. To fill up these existing data gaps and to address ambiguities and discrepancies, the current study focuses on giving preliminary insights on safety assessment of FD and their potential targeted by-products. With the available information in the literature and obtained experimental results, physicochemical safety, environmental safety as well as (a scenario based) fire safety profiles of key FD, as well as side streams such as humins and levulinic acid, will be considered. With this, the study focuses on defining patterns and trends that gives coherent safety related information for existing and newly synthesized FD in the market for better functionality and sustainable applications.

Keywords: furanics, humins, safety, thermal and fire hazard, toxicity

Procedia PDF Downloads 169

Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

Abstract:

Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

Procedia PDF Downloads 111

Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

Abstract:

In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

Procedia PDF Downloads 313

Authors: Kajal Zarei

Abstract:

Supply chain management is becoming an increasingly important issue in many businesses today. In such circumstances, a number of reasons such as management deficiency in different segments of the supply chain, lack of streamlined processes, resistance to change the current systems and technologies, and lack of advanced information system have paved the ground to ask for innovative research studies. To this end, information technology (IT) is becoming a major driver to overcome the supply chain limitations and deficiencies. The emergence of IT has provided an excellent opportunity for redefining the supply chain to be more effective and competitive. This paper has investigated the IT impact on two-digit industry codes in the International Standard Industrial Classification (ISIC) that are operating in four groups of the supply chains. Firstly, the primary fields of the supply chain were investigated, and then paired comparisons of different industry parts were accomplished. Using experts' ideas and Analytical Hierarchy Process (AHP), the status of industrial activities in Kurdistan Province in Iran was determined. The results revealed that manufacturing and inventory fields have been more important compared to other fields of the supply chain. In addition, IT has had greater impact on food and beverage industry, chemical industry, wood industry, wood products, and production of basic metals. The results indicated the need to IT awareness in supply chain management; in other words, IT applications needed to be developed for the identified industries.

Keywords: supply chain, information technology, analytical hierarchy process, two-digit codes, international standard industrial classification

Procedia PDF Downloads 283

Authors: Dhvanidevi N. Jadeja, Daya S. Kaul, Anurag A. Kandya

Abstract:

Municipal Solid Waste (MSW) collection in a city is performed in less effective manner which results in the poor management of the environment and natural resources. Municipal corporation does not possess efficient waste management and recycling programs because of the complex task involving many factors. Solid waste collection system depends upon various factors such as manpower, number and size of vehicles, transfer station size, dustbin size and weight, on-road traffic, and many others. These factors affect the collection cost, energy and overall municipal tax for the city. Generally, different types of waste are scattered throughout the city in a heterogeneous way that poses changes for efficient collection of solid waste. Efficient waste collection and transportation strategy must be effectively undertaken which will include optimization of routes, volume of waste, and manpower. Being these optimized, the overall cost can be reduced as the fuel and energy requirements would be less and also the municipal waste taxes levied will be less. To carry out the optimization study of collection system various data needs to be collected from the Ahmedabad municipal corporation such as amount of waste generated per day, number of workers, collection schedule, road maps, number of transfer station, location of transfer station, number of equipment (tractors, machineries), number of zones, route of collection etc. The ArcGis Network Analyst is introduced for the best routing identification applied in municipal waste collection. The simulation consists of scenarios of visiting loading spots in the municipality of Ahmedabad, considering dynamic factors like network traffic changes, closed roads due to natural or technical causes. Different routes were selected in a particular area of Ahmedabad city, and present routes were optimized to reduce the length of the routes, by using ArcGis Network Analyst. The result indicates up to 35% length minimization in the routes.

Keywords: collection routes, efficiency, municipal solid waste, optimization

Procedia PDF Downloads 137

Authors: Leng Jian-Wei, Wang Lai-Jun, Li Ye

Abstract:

In order to measure and analyze the transportation situation within the counties of Shaanxi province over a certain period of time and to promote the province's future transportation planning and development, this paper proposes a reasonable layout plan and compares model rationality. The study uses entropy weight method to measure the transportation advantages of 107 counties in Shaanxi province from three dimensions: road network density, trunk line influence and location advantage in 2013 and 2021, and applies spatial autocorrelation analysis method to analyze the spatial layout and development trend of county-level transportation, and conducts ordinary least square (OLS)regression on transportation impact factors and other influencing factors. The paper also compares the regression fitting degree of the Geographically weighted regression(GWR) model and the OLS model. The results show that spatially, the transportation advantages of Shaanxi province generally show a decreasing trend from the Weihe Plain to the surrounding areas and mainly exhibit high-high clustering phenomenon. Temporally, transportation advantages show an overall upward trend, and the phenomenon of spatial imbalance gradually decreases. People's travel demands have changed to some extent, and the demand for rapid transportation has increased overall. The GWR model regression fitting degree of transportation advantages is 0.74, which is higher than the OLS regression model's fitting degree of 0.64. Based on the evolution of transportation advantages, it is predicted that this trend will continue for a period of time in the future. To improve the transportation advantages of Shaanxi province increasing the layout of rapid transportation can effectively enhance the transportation advantages of Shaanxi province. When analyzing spatial heterogeneity, geographic factors should be considered to establish a more reliable model

Keywords: traffic dominance, GWR model, spatial autocorrelation analysis, temporal and spatial evolution

Procedia PDF Downloads 90

Authors: K. Sridhar Reddy, M. Amaranatha Reddy, Nilanjan Mitra

Abstract:

Short paneled concrete pavement (SPCP) with shorter panel size can be an alternative to the conventional jointed plain concrete pavements (JPCP) at the same cost as the asphalt pavements with all the advantages of concrete pavement with reduced thickness, less chance of mid-slab cracking and or dowel bar locking so common in JPCP. Cast-in-situ short concrete panels (short slabs) laid on a strong foundation consisting of a dry lean concrete base (DLC), and cement treated subbase (CTSB) will reduce the thickness of the concrete slab to the order of 180 mm to 220 mm, whereas JPCP was with 280 mm for the same traffic. During the construction of SPCP test sections on two Indian National Highways (NH), it was observed that the joints remain uncracked after a year of traffic. The undoweled and uncracked joints load transfer variability and joint behavior are of interest with anticipation on its long-term performance of the SPCP. To investigate the effects of undoweled and uncracked joints on short slabs, the present study was conducted. A multilayer linear elastic analysis using 3D finite element package for different panel sizes with different thicknesses resting on different types of solid elastic foundation with and without temperature gradient was developed. Surface deflections were obtained from 3D FE model and validated with measured field deflections from falling weight deflectometer (FWD) test. Stress analysis indicates that flexural stresses in short slabs are decreased with a decrease in panel size and increase in thickness. Detailed evaluation of stress analysis with the effects of curling behavior, the stiffness of the base layer and a variable degree of load transfer, is underway.

Keywords: joint behavior, short slabs, uncracked joints, undoweled joints, 3D numerical simulation

Procedia PDF Downloads 183

Authors: Kamakshaiah Kolli, R. Seshadri

Abstract:

Water is a key resource in all economic activities ranging from agriculture to industry. Only a tiny fraction of the planet's abundant water is available to us as fresh water. Assessment of water quality has always been paramount in the field of environmental quality management. It is the foundation for health, hygiene, progress and prosperity. With ever increasing pressure of human population, there is severe stress on water resources. Therefore efficient water management is essential to civil society for betterment of quality of life. The present study emphasizes on the groundwater quality, sources of ground water contamination, variation of groundwater quality and its spatial distribution. The bases for groundwater quality assessment are groundwater bodies and representative monitoring network enabling determination of chemical status of groundwater body. For this study, water samples were collected from various areas of the entire corporation area of Guntur. Water is required for all living organisms of which 1.7% is available as ground water. Water has no calories or any nutrients, but essential for various metabolic activities in our body. Chemical and physical parameters can be tested for identifying the portability of ground water. Electrical conductivity, pH, alkalinity, Total Alkalinity, TDS, Calcium, Magnesium, Sodium, Potassium, Chloride, and Sulphate of the ground water from Guntur district: Different areas of the District were analyzed. Our aim is to check, if the ground water from the above areas are potable or not. As multivariate are present, Data mining technique using JRIP rules was employed for classifying the ground water.

Keywords: groundwater, water quality standards, potability, data mining, JRIP, PCA, classification

Procedia PDF Downloads 432

Authors: Manal Hassan Abdel Aziz, Fatma Mohamed Magdy Badr El Dine, Nourhan Mohamed Mohamed Saeed

Abstract:

Establishing association between lip print patterns and those of fingerprints as well as blood groups is of fundamental importance in the forensic identification domain. The first aim of the current study was to determine the prevalent types of ABO blood groups, lip prints and fingerprints patterns in both studied populations. Secondly, to analyze any relation found between the different print patterns and the blood groups, which would be valuable in identification purposes. The present study was conducted on 60 healthy volunteers, (30 males and 30 females) from each of the studied population. Lip prints and fingerprints were obtained and classified according to Tsuchihashi's classification and Michael Kuchen’s classification, respectively. The results show that the ulnar loop was the most frequent among both populations. Blood group A was the most frequent among Egyptians, while blood groups O and B were the predominant among Malaysians. Significant relations were observed between lip print patterns and fingerprint (in the second quadrant for Egyptian males and the first one for Malaysian). For Malaysian females, a statistically significant association was proved in the fourth quadrant. Regarding the blood groups, 89.5% of ulnar loops were significantly related to blood group A among Egyptian males. The results proved an association between the fingerprint pattern and the lip prints, as well as between the ABO blood group and the pattern of fingerprints. However, further researches with larger sample sizes need to be directed to approve the current results.

Keywords: ABO, cheiloscopy, dactylography, Egyptians, Malaysians

Procedia PDF Downloads 222

Authors: Yue Wang, Fan Chen

Abstract:

After the economic reform in 1978, the urbanization in China has grown fast. It urged cities to expand in an unprecedented high speed. Villages around were annexed unprepared, and it turned out to be a new type of community called 'village in city.' Two things happened here. First, the locals gave up farming and turned to secondary industry and tertiary industry, as a result of losing their land. Secondly, attracted by the high income in cities and low rent here, plenty of migrants came into the community. This area is important to a city in rapid growth for providing a transitional zone. But thanks to its passivity and low development, 'village in city' has caused lots of trouble to the city. Densities of population and construction are both high, while facilities are severely inadequate. Unplanned and illegal structures are built, which creates a complex mixed-function area and leads to a bad residential area. Besides, the locals have a strong property right consciousness for the land. It holds back the transformation and development of the community. Although the land capitalization can bring significant benefits, it’s inappropriate to make a great financial compensation to the locals, and considering the large population of city migrants, it’s important to explore the relationship among the 'village in city,' city immigrants and the city itself. Taking the example of Hangzhou, this paper analyzed the developing process, functions spatial distribution, industrial structure and current traffic system of 'village in city.' Above the research on the community, this paper put forward a common method to make urban planning through the following ways: adding city functions, building civil facilities, re-planning functions spatial distribution, changing the constitution of local industry and planning new traffic system. Under this plan, 'village in city' finally can be absorbed into cities and make its own contribution to the urbanization.

Keywords: China, city immigrant, urbanization, village in city

Procedia PDF Downloads 217

Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

Abstract:

The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

Procedia PDF Downloads 285

Authors: Kyoungwoon Kim, Cuong Huynh, Cam Nguyen

Abstract:

Exploding demands for more data, faster data transmission speed, less interference, more users, more wireless devices, and better reliable service-far exceeding those provided in the current mobile communications networks in the RF spectrum below 6 GHz-has led the wireless communication industry to focus on higher, previously unallocated spectrums. High frequencies in RF spectrum near (around 28 GHz) or within the millimeter-wave regime is the logical solution to meet these demands. This high-frequency RF spectrum is of increasingly important for wireless communications due to its large available bandwidths that facilitate various applications requiring large-data high-speed transmissions, reaching up to multi-gigabit per second, of vast information. It also resolves the traffic congestion problems of signals from many wireless devices operating in the current RF spectrum (below 6 GHz), hence handling more traffic. Consequently, the wireless communication industries are moving towards 5G (fifth generation) for next-generation communications such as mobile phones, autonomous vehicles, virtual reality, and the Internet of Things (IoT). The U.S. Federal Communications Commission (FCC) proved on 14th July 2016 three frequency bands for 5G around 28, 37 and 39 GHz. We present some silicon-based RFIC power amplifiers (PA) for possible implementation for 5G wireless communications around 28, 37 and 39 GHz. The 16.5-28 GHz PA exhibits measured gain of more than 34.5 dB and very flat output power of 19.4±1.2 dBm across 16.5-28 GHz. The 25.5/37-GHz PA exhibits gain of 21.4 and 17 dB, and maximum output power of 16 and 13 dBm at 25.5 and 37 GHz, respectively, in the single-band mode. In the dual-band mode, the maximum output power is 13 and 9.5 dBm at 25.5 and 37 GHz, respectively. The 10-19/23-29/33-40 GHz PA has maximum output powers of 15, 13.3, and 13.8 dBm at 15, 25, and 35 GHz, respectively, in the single-band mode. When this PA is operated in dual-band mode, it has maximum output powers of 11.4/8.2 dBm at 15/25 GHz, 13.3/3 dBm at 15/35 GHz, and 8.7/6.7 dBm at 25/35 GHz. In the tri-band mode, it exhibits 8.8/5.4/3.8 dBm maximum output power at 15/25/35 GHz. Acknowledgement: This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Keywords: Microwaves, Millimeter waves, Power Amplifier, Wireless communications

Procedia PDF Downloads 190

Authors: Jairson Barbosa Rodrigues, Paulo Romero Martins Maciel, Germano Crispim Vasconcelos

Abstract:

This paper presents an investigation of the performance impacts regarding the variation of five factors (input data size, node number, cores, memory, and disks) when applying a distributed implementation of Naïve Bayes for text classification of a large Corpus on the Spark big data processing framework. Problem: The algorithm's performance depends on multiple factors, and knowing before-hand the effects of each factor becomes especially critical as hardware is priced by time slice in cloud environments. Objectives: To explain the functional relationship between factors and performance and to develop linear predictor models for time and cost. Methods: the solid statistical principles of Design of Experiments (DoE), particularly the randomized two-level fractional factorial design with replications. This research involved 48 real clusters with different hardware arrangements. The metrics were analyzed using linear models for screening, ranking, and measurement of each factor's impact. Results: Our findings include prediction models and show some non-intuitive results about the small influence of cores and the neutrality of memory and disks on total execution time, and the non-significant impact of data input scale on costs, although notably impacts the execution time.

Keywords: big data, design of experiments, distributed machine learning, natural language processing, spark

Procedia PDF Downloads 121

Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

Abstract:

Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

Procedia PDF Downloads 136

Authors: Q. Toufeeq, R. Kauser, U. R. Jamil, N. Sohaib

Abstract:

Foundation design of a structure needs soil investigation to avoid failures due to settlements. This soil investigation is expensive and time-consuming. Developments of new residential societies involve huge leveling of large sites that is accompanied by heavy land filling. Poor practices of land fill for deep depths cause differential settlements and consolidations of underneath soil that sometimes result in the collapse of structures. The extent of filling remains unknown to the individual developer unless soil investigation is carried out. Soil investigation cannot be performed on each available site due to involved costs. However, fair estimate of bearing capacity can be made if such tests are already done in the surrounding areas. The geotechnical guidance maps can provide a fair assessment of soil properties. Previously, GIS-based approaches have been used to develop maps using extrapolation and interpolations techniques for bearing capacities, underground recharge, soil classification, geological hazards, landslide hazards, socio-economic, and soil liquefaction mapping. Standard penetration test (SPT) data of surrounding sites were already available. Google Earth is used for digitization of collected data. Few points were considered for data calibration and validation. Resultant Geographic information system (GIS)-based guidance maps are helpful to anticipate the bearing capacity in the real estate industry.

Keywords: bearing capacity, soil classification, geographical information system, inverse distance weighted, radial basis function

Procedia PDF Downloads 137

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

Abstract:

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 500

Authors: Sifiso Vilakazi, Lerato Mzenzi

Abstract:

BACKGROUND :- Human mind is a double-edged sword, it is both arduous and obtuse, vague and explicit. The neuroscience of brain has baffled all and left none. In this labyrinth, a novel neurotransmitter has set its foot. D-Aspartic acid, which has been established in rats and animals’ brain as a neurochemical in controlling behaviour, aggression, fertility, cognition and memory is set to formulate a new benchmark as a marker among suicidal and accidental death victims. Without losing sight of the fact, we have endeavoured to dig out the biochemical relation behind suicide in our research. We in our research have exerted to decipher the puzzle among humans for D-Aspartic acid using HPLC. METHODS:- We have taken 30 samples each of pre-frontal cortex of brain from suicide and road traffic accidents victims for our case-control research to establish brain D-Aspartic acid level using HPLC as a marker for suicide. RESULTS:- By using Independent/Unpaired T-Test Analysis:- The two-tailed P value is less than 0.0044. MEAN (mMOL/mL/G) for D-Aspartic acid in control is 24.2737±7.5958 and for cases is 19.5540±4.2647. By conventional criteria, this difference is considered to be extremely statistically significant. CONCLUSION:- (1) Brain D-Aspartic acid has an effect in the act of suicide and its values show a large drop before the act of suicide is commenced as compared to road traffic accidents which correlates with the study where D-AA was studied as a novel endogenous neurotransmitter in case of mental health disorders in humans, though never studied on suicide victims, likely getting it reference from research occurring on rats where it was discovered that D-AA plays a role in cognition, personality development, thought process, aggression. (2)The other study which was related to humans and signified the role of D-AA in reproduction, where low D-AA leads to infertility and depression and hence providing the much needed proof of role of D-AA in mental health disorders and suicide, and subsequently to the act of suicide.

Keywords: green energy, policy, technology, implementation

Procedia PDF Downloads 11

Authors: Randa Ismail Sharafeddine

Abstract:

The Islamic economic system is distinctive in that it implicitly recognizes money as a separate, independent component of production capable of assuming risk and so entitled to the same reward as other Entrepreneurial Factors of Production (EFP). Conventional theory does not identify money capital explicitly as a component of production; rather, interest is recognized as a reward for capital, the interest rate is the cost of money capital, and it is also seen as a cost of physical capital. The conventional theory of production examines how diverse non-entrepreneurial resources (Land, Labor, and Capital) are selected; however, the economic theory community is largely unaware of the reasons why these resources choose to remain as non-entrepreneurial resources as opposed to becoming entrepreneurial resources. Should land, labor, and financial asset owners choose to work for others in return for rent, income, or interest, or should they engage in entrepreneurial risk-taking in order to profit. This is a decision made often in the actual world, but it has never been effectively treated in economic theory. This article will conduct a critical analysis of the conventional classification of factors of production and propose a classification for resource allocation and income distribution (Rent, Wages, Interest, and Profits) that is more rational, even within the conventional theoretical framework for evaluating and developing production and distribution theories. Money is an essential component of production in an Islamic economy, and it must be used to sustain economic activity.

Keywords: financial capital, production theory, distribution theory, economic activity, riba avoidance, institution of participation

Procedia PDF Downloads 95

Authors: Hitesh Kshayap, Shantanu Arya, Ajay Basod, Sachin Sakhuja

Abstract:

This study was undertaken to measure the overall noise levels in different locations/zones and to estimate the prevalence of Noise induced hearing loss in Hawkers & Shopkeepers in Mumbai, India. The Hearing Test developed by American Academy Of Otolaryngology, translated from English to Hindi, and validated is used as a screening tool for hearing sensitivity was employed. The tool is having 14 items. Each item is scored on a scale 0, 1, 2 and 3. The score 6 and above indicated some difficulty or definite difficulty in hearing in daily activities and low score indicated lesser difficulty or normal hearing. The subjects who scored 6 or above or having tinnitus were made to undergo hearing evaluation by Pure tone audiometer. Further, the environmental noise levels were measured from Morning to Evening at road side at different Location/Hawking zones in Mumbai city using SLM9 Agronic 8928B & K type Digital Sound Level Meter) in dB (A). The maximum noise level of 100.0 dB (A) was recorded during evening hours from Chattrapati Shivaji Terminal to Colaba with overall noise level of 79.0 dB (A). However, the minimum noise level in this area was 72.6 dB (A) at any given point of time. Further, 54.6 dB (A) was recorded as minimum noise level during 8-9 am at Sion Circle. Further, commencement of flyovers with 2-tier traffic, sky walks, increasing number of vehicular traffic at road, high rise buildings and other commercial & urbanization activities in the Mumbai city most probably have resulted in increasing the overall environmental noise levels. Trees which acted as noise absorbers have been cut owing to rapid construction. The study involved 100 participants in the age range of 18 to 40 years of age, with the mean age of 29 years (S.D. =6.49). 46 participants having tinnitus or have obtained the score of 6 were made to undergo Pure Tone Audiometry and it was found that the prevalence rate of hearing loss in hawkers & shopkeepers is 19% (10% Hawkers and 9 % Shopkeepers). The results found indicates that 29 (42.6%) out of 64 Hawkers and 17 (47.2%) out of 36 Shopkeepers who underwent PTA had no significant difference in percentage of Noise Induced Hearing loss. The study results also reveal that participants who exhibited tinnitus 19 (41.30%) out of 46 were having mild to moderate sensorineural hearing loss between 3000Hz to 6000Hz. The Pure tone Audiogram pattern revealed Hearing loss at 4000 Hz and 6000 Hz while hearing at adjacent frequencies were nearly normal. 7 hawkers and 8 shopkeepers had mild notch while 3 hawkers and 1 shopkeeper had a moderate degree of notch. It is thus inferred that tinnitus is a strong indicator for presence of hearing loss and 4/6 KHz notch is a strong marker for road/traffic/ environmental noise as an occupational hazard for hawkers and shopkeepers. Mass awareness about these occupational hazards, regular hearing check up, early intervention along with sustainable development juxtaposed with social and urban forestry can help in this regard.

Keywords: NIHL, noise, sound level meter, tinnitus

Procedia PDF Downloads 204

Authors: Mst Ilme Faridatul, Bo Wu

Abstract:

Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.

Keywords: land cover, mapping, multi-temporal, spectral indices

Procedia PDF Downloads 155

Authors: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

Abstract:

Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

Procedia PDF Downloads 34

Authors: Usama Shahid, Muhammad Zunnurain Hussain

Abstract:

This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

Procedia PDF Downloads 103

Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

Abstract:

The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

Procedia PDF Downloads 118