Search results for: waste classification

Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

Procedia PDF Downloads 395

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

Procedia PDF Downloads 91

Authors: Ouaaz Oum Essaad, Melbouci Bachir

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The experimental study conducted has a dual purpose: to valorize the clay material in the road domain and improve the lift of the shape layers by strengthening with plastic waste (in the form of aggregates). To do this, six mixtures of Clay and sand of different percentages were studied: 100% Clay, 95% Clay + 05% Sand, 90% Clay + 10% Sand, 85% Clay + 15% Sand, 80% Clay + 20% Sand, 75% Clay + 25% Sand. Proctor compaction and simple compression tests have been carried out on mixtures (sand + clay + plastic waste). The results obtained show a clear evolution of the characteristics of the Proctor test and the compressive strength of the mixtures according to the different types and percentages of the recycled plastic Plasticity and consistency index are important parameters that play a role in the toughness of plastic soil.

Keywords: valorization, recycling, soil mixture, mechanical tests

Procedia PDF Downloads 90

Authors: Malek Miladi, Miguel Vegara, Maria Perez-Infantes, Khaled Mohamed Ramadan, Antonio Ruiz-Canales, Damaris Nunez-Gomez

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Coffee is the second consumed commodity worldwide, yet it also generates colossal waste. Proper management of coffee waste is proposed by converting them into products with higher added value to achieve sustainability of the economic and ecological footprint and protect the environment. Based on this, a study looking at the recovery of coffee waste is becoming more relevant in recent decades. Spent coffee grounds (SCG's) resulted from brewing coffee represents the major waste produced among all coffee industry. The fact that SCGs has no economic value be abundant in nature and industry, do not compete with agriculture and especially its high oil content (between 7-15% from its total dry matter weight depending on the coffee varieties, Arabica or Robusta), encourages its use as a sustainable feedstock for bio-oil production. The bio-oil extraction is a crucial step towards biodiesel production by the transesterification process. However, conventional methods used for oil extraction are not recommended due to their high consumption of energy, time, and generation of toxic volatile organic solvents. Thus, finding a sustainable, economical, and efficient extraction technique is crucial to scale up the process and to ensure more environment-friendly production. Under this perspective, the aim of this work was the statistical study to know an efficient strategy for oil extraction by n-hexane using indirect sonication. The coffee waste mixed Arabica and Robusta, which was used in this work. The temperature effect, sonication time, and solvent-to-solid ratio on the oil yield were statistically investigated as dependent variables by Central Composite Rotatable Design (CCRD) 23. The results were analyzed using STATISTICA 7 StatSoft software. The CCRD showed the significance of all the variables tested (P < 0.05) on the process output. The validation of the model by analysis of variance (ANOVA) showed good adjustment for the results obtained for a 95% confidence interval, and also, the predicted values graph vs. experimental values confirmed the satisfactory correlation between the model results. Besides, the identification of the optimum experimental conditions was based on the study of the surface response graphs (2-D and 3-D) and the critical statistical values. Based on the CCDR results, 29 ºC, 56.6 min, and solvent-to-solid ratio 16 were the better experimental conditions defined statistically for coffee waste oil extraction using n-hexane as solvent. In these conditions, the oil yield was >9% in all cases. The results confirmed the efficiency of using an ultrasound bath in extracting oil as a more economical, green, and efficient way when compared to the Soxhlet method.

Keywords: coffee waste, optimization, oil yield, statistical planning

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Authors: Yi-Hao Pai, Wen-Feng Chen

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The planting area of Pomelo in Hualien, Taiwan amounts to thousands of hectares. Especially in the blooming season of Pomelo, it is an important producing area for Pomelo honey, and it is also a good test field for promoting the "Under-forest Economy". However, in the current Pomelo garden planting and management operations, the large amount of agricultural waste generated by the pruning of the branches causes environmental sanitation concerns, which can lead to the hiding of pests or the infection of the Pomelo tree, and indirectly increase the health risks of bees. Therefore, how to deal with the pruning of the branches and avoid open burning is a topic of social concern in recent years. In this research, afeasibility study evaluating energy conversion efficiency through agricultural waste gasification from the Pomelo garden, Taiwan, is demonstrated. we used a high-temperature gasifier to convert the pruning of the branches into syngas and biochar. In terms of syngas composition and calorific value assessment, we use the biogas monitoring system for analysis. Then, we used Raman spectroscopy and electron microscopy (EM) to diagnose the microstructure and surface morphology of biochar. The results indicate that the 1 ton of pruning of the branches can produce 1797.03m3 of syngas, corresponding to a calorific value of 9.1MJ/m3. The main components of the gas include CH4, H2, CO, and CO2, and the corresponding gas composition ratio is 16.8%, 7.1%, 13.7%, and 24.5%. Through the biomass syngas generator with a conversion efficiency of 30% for power generation, a total of 1,358kWh can be obtained per ton of pruning of the branches. In the research of biochar, its main characteristics in Raman spectroscopy are G bands and D bands. The first-order G and D bands are at 1580 and 1350 cm⁻¹, respectively. The G bands originates from the in-plane tangential stretching of the C−C bonds in the graphitic structure, and theD band corresponds to scattering from local defects or disorders present in carbon. The area ratio of D and G peaks (D/G) increases with the decrease of reaction temperature. The larger the D/G, the higher the defect concentration and the higher the porosity. This result is consistent with the microstructure displayed by SEM. The study is expected to be able to reuse agricultural waste and promote the development of agricultural and green energy circular economy.

Keywords: agricultural waste, gasification, energy conversion, pomelo garden

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Authors: Harold Jideofor

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Wastewater treatment consists of applying known technology to improve or upgrade the quality of a wastewater. Usually wastewater treatment will involve collecting the wastewater in a central, segregated location (the Wastewater Treatment Plant) and subjecting the wastewater to various treatment processes. Most often, since large volumes of wastewater are involved, treatment processes are carried out on continuously flowing wastewaters (continuous flow or "open" systems) rather than as "batch" or a series of periodic treatment processes in which treatment is carried out on parcels or "batches" of wastewaters. While most wastewater treatment processes are continuous flow, certain operations, such as vacuum filtration, involving storage of sludge, the addition of chemicals, filtration and removal or disposal of the treated sludge, are routinely handled as periodic batch operations.

Keywords: wastewater treatment, environmental engineering, waste water

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Authors: Boce Zhang, Yaguang Luo

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Ripening of climacteric fruits, such as bananas and avocados, are usually initiated days prior to the retail marketing. However, upon the onset of irreversible ripening, they undergo rapid spoilage if not consumed within a narrow climacteric time window. Controlled ripening of climacteric fruits is a critical step to provide consumers with high-quality products while reducing postharvest losses and food waste. There is a high demand for technologies that can retard the ripening process or enable accelerated ripening immediately before consumption. In this work, metal−organic framework (MOF) was developed as a solid porous matrix to encapsulate gaseous hormone, including ethylene, for subsequent application. The feasibility of the on-demand stimulated ripening of bananas and avocados is also evaluated. MOF was synthesized and loaded with ethylene gas. The MOF−ethylene was placed inside sealed containers with preclimacteric bananas and avocados and stored at 16 °C. The fruits were treated for 24-48 hours, and evaluated for ripening progress. Results indicate that MOF−ethylene treatment significantly accelerated the ripening-related changes of color and textural properties in treated bananas and avocados. The average ripening period for both avocados and bananas were reduced in half by using this method. No significant differences of quality characteristics at respective ripening stages were observed between produce ripened via MOF-ethylene versus exogenously supplied ethylene gas or endogenously produced ethylene. Solid MOF matrices could have multiple advantages compared to existing systems, including easy to transport and safe to use by minimally trained produce handlers and consumers. We envision that this technology can help tackle food waste challenges at the critical retail and consumer stages in the food supply chain.

Keywords: climacteric produce, controllable ripening, food waste challenge, metal organic framework

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Authors: Horng-Wen Wu, Yi Chao, Rong-Fang Horng

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This study is to develop a methanol steam reformer with a heat recovery zone, which recovers heat from exhaust gas of a diesel engine, and to investigate waste heat recovery ratio at the required reaction temperature. The operation conditions of the reformer are reaction temperature (200 °C, 250 °C, and 300 °C), steam to carbonate (S/C) ratio (0.9, 1.1, and 1.3), and N2 volume flow rate (40 cm3/min, 70 cm3/min, and 100 cm3/min). Finally, the hydrogen concentration, the CO, CO2, and N2 concentrations are measured and recorded to calculate methanol conversion efficiency, hydrogen flow rate, and assisting combustion gas and impeding combustion gas ratio. The heat source of this reformer comes from electric heater and waste heat of exhaust gas from diesel engines. The objective is to recover waste heat from the engine and to make more uniform temperature distribution within the reformer. It is beneficial for the reformer to enhance the methanol conversion efficiency and hydrogen-rich gas production. Experimental results show that the highest hydrogen flow rate exists at N2 of the volume rate 40 cm3/min and reforming reaction temperature of 300 °C and the value is 19.6 l/min. With the electric heater and heat recovery from exhaust gas, the maximum heat recovery ratio is 13.18 % occurring at water-methanol (S/C) ratio of 1.3 and the reforming reaction temperature of 300 °C.

Keywords: heat recovery, hydrogen-rich production, methanol steam reformer, methanol conversion efficiency

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Authors: Mubashir Hussain, Mu Lv, Xiaohan Dong, Zhiyang Li, Bin Liu, Nongyue He

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Detection and classification of microbes have a vast range of applications in biomedical engineering especially in detection, characterization, and quantification of bacterial contaminants. For identification of pathogens, different techniques are emerging in the field of biomedical engineering. Latest technology uses light scattering, capable of identifying different pathogens without any need for biochemical processing. Bacterial Pathogens Identification System (BPIS) which uses a laser beam, passes through the sample and light scatters off. An assembly of photodetectors surrounded by the sample at different angles to detect the scattering of light. The algorithm of the system consists of two parts: (a) Library files, and (b) Comparator. Library files contain data of known species of bacterial microbes in the form of binned plots, while comparator compares data of unknown sample with library files. Using collected data of unknown bacterial species, highest voltage values stored in the form of peaks and arranged in 3D histograms to find the frequency of occurrence. Resulting data compared with library files of known bacterial species. If sample data matching with any library file of known bacterial species, sample identified as a matched microbe. An experiment performed to identify three different bacteria particles: Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. By applying algorithm using library files of given samples, results were compromising. This system is potentially applicable to several biomedical areas, especially those related to cell morphology.

Keywords: microbial identification, laser scattering, peak identification, binned plots classification

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Authors: Mohamad H. Atyeh, Ahmad Khaldi

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The main objective of this research is to measure the relationship between the Kuwait stock Exchange (KSE) index and its two marketing sectors after the new market classification. The findings of this research are important for Public economic policy makers as they need to know if the new system (new classification) is efficient and to what level, to monitor the markets and intervene with appropriate measures. The data used are the daily index of the whole Kuwaiti market and the daily closing price, number of deals and volume of shares traded of two marketing sectors (consumer goods and consumer services) for the period from the 13th of May 2012 till the 12th of December 2016. The results indicate a positive direct impact of the closing price, volume and deals indexes of the consumer goods and the consumer services companies on the overall KSE index, volume and deals of the Kuwaiti stock market (KSE).

Keywords: correlation, market capitalization, Kuwait Stock Exchange (KSE), marketing sectors, stock performance

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Rachid Oulad Haj Thami, Sana El Fkihi

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Color and texture are highly discriminant visual cues that provide an essential information in many types of images. Color texture representation and classification is therefore one of the most challenging problems in computer vision and image processing applications. Color textures can be represented in different color spaces by using multiple image descriptors which generate a high dimensional set of texture features. In order to reduce the dimensionality of the feature set, feature selection techniques can be used. The goal of feature selection is to find a relevant subset from an original feature space that can improve the accuracy and efficiency of a classification algorithm. Traditionally, feature selection is focused on removing irrelevant features, neglecting the possible redundancy between relevant ones. This is why some feature selection approaches prefer to use feature clustering analysis to aid and guide the search. These techniques can be divided into two categories. i) Feature clustering-based ranking algorithm uses feature clustering as an analysis that comes before feature ranking. Indeed, after dividing the feature set into groups, these approaches perform a feature ranking in order to select the most discriminant feature of each group. ii) Feature clustering-based subset search algorithms can use feature clustering following one of three strategies; as an initial step that comes before the search, binded and combined with the search or as the search alternative and replacement. In this paper, we propose a new feature clustering-based sequential selection approach for the purpose of color texture representation and classification. Our approach is a three step algorithm. First, irrelevant features are removed from the feature set thanks to a class-correlation measure. Then, introducing a new automatic feature clustering algorithm, the feature set is divided into several feature clusters. Finally, a sequential search algorithm, based on a filter model and a separability measure, builds a relevant and non redundant feature subset: at each step, a feature is selected and features of the same cluster are removed and thus not considered thereafter. This allows to significantly speed up the selection process since large number of redundant features are eliminated at each step. The proposed algorithm uses the clustering algorithm binded and combined with the search. Experiments using a combination of two well known texture descriptors, namely Haralick features extracted from Reduced Size Chromatic Co-occurence Matrices (RSCCMs) and features extracted from Local Binary patterns (LBP) image histograms, on five color texture data sets, Outex, NewBarktex, Parquet, Stex and USPtex demonstrate the efficiency of our method compared to seven of the state of the art methods in terms of accuracy and computation time.

Keywords: feature selection, color texture classification, feature clustering, color LBP, chromatic cooccurrence matrix

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Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

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Authors: M. Parvez Alam, M. Bilal Khan

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Concrete is the material of choice where strength, performance, durability, impermeability, fire resistance, and abrasion resistance are required. The hunger for the higher strength leads to other materials to achieve the desired results and thus, emerged the contribution of cementitious material for the strength of concrete In present day constructions, concrete is chosen as one of the best choices by civil engineers in construction materials. The concept of sustainability is touching new heights and many pozzolonic materials are tried and tested as partial replacement for the cement. In this paper, comprehensive review of available literatures are studied to evaluate the performance of pozzolonic materials such as ceramic waste powder, copper slag, silica fume on the strength of concrete by the partial replacement of ordinary materials such as cement, fine aggregate and coarse aggregate at different percentage of composition. From the study, we conclude that ceramic wastes are suitable to be used in the construction industry, and more significantly on the making of concrete. Ceramic wastes are found to be suitable for usage as substitution for fine and coarse aggregates and partial substitution in cement production. They were found to be performing better than normal concrete, in properties such as density, durability, permeability, and compressive strength. Copper slag is the waste material of matte smelting and refining of copper such that each ton of copper generates approximately 2.5 tons of copper slag. Copper slag is one of the materials that is considered as a waste which could have a promising future in construction Industry as partial or full substitute of aggregates. Silica fume, also known as micro silica or condensed silica fume, is a relatively new material compared to fly ash, It is another material that is used as an artificial pozzolonic admixture. High strength concrete made with silica fume provides high abrasion/corrosion resistance.

Keywords: concrete, pozzolonic materials, ceramic waste powder, copper slag

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Authors: Patel Darshan Shaileshkumar, M. G. Vanza

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Black cotton soil is problematic soil for any construction work. Black cotton soil contains montmorillonite in its structure. Due to this mineral, black cotton soil will attain maximum swelling and shrinkage. Due to these volume changes, it is necessary to stabilize black cotton soil before the construction of the road. For soil stabilization use of pozzolanic waste is found to be a good solution by some researchers. The palm kernel shell ash (PKSA) is a pozzolanic material that can be used for soil stabilization. Basically, PKSA is a waste material, and it is available at a cheap cost. Palm kernel shell is a waste material generated in palm oil mills. Then palm kernel shell is used in industries instead of coal for power generation. After the burning of a palm kernel shell, ash is formed; the ash is called palm kernel shell ash (PKSA). The PKSA contains a free lime content that will react chemically with the silicate and aluminate of black cotton soil and forms a C-S-H and C-A-H gel which will bines soil particles together and reduce the plasticity of the soil. In this study, the PKSA is added to the soil. It was found that with the addition of PKSA content in the soil, the liquid limit of the soil is decreased, the plastic limit of the soil is increased, and the plasticity of the soil is decreased. The group index value of the soil is evaluated, and it was found that with the addition of PKSA GI value of the soil is decreased, which indicates the strength of the soil is improved.

Keywords: palm kernel shell ash, black cotton soil, liquid limit, group index, plastic limit, plasticity index

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Mohieldin Elmashad, Salwa Yassin

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Expansive soils regarded as one of the most problematic unsaturated formations in the Egyptian arid zones and present a great challenge in civil engineering, in general, and geotechnical engineering, in particular. Severe geotechnical complications and consequent structural damages have been arising due to an excessive and differential volumetric change upon wetting and change in water content. Different studies have been carried out concerning the swelling performance of the expansive soils using different additives including phospho-gypsum as an industrial waste product. However, this paper describes the results of a comprehensive testing programme that was carried out to investigate the effect of phospho-gypsum (PG) and sodium chloride (NaCl), as an additive mixture, on the swelling performance of constituent samples of swelling soils. The constituent samples comprise commercial bentonite collected from a natural site, mixed with different percentages of PG-NaCl mixture. The testing programme had been scoped to cover the physical and chemical properties of the constituent samples. In addition, a mineralogical study using x-ray diffraction (XRD) was performed on the collected bentonite and the mixed bentonite with PG-NaCl mixture samples. The obtained results of this study showed significant improvement in the swelling performance of the tested samples with the increase of the proposed PG-NaCl mixture content.

Keywords: expansive soils, industrial waste, mineralogical study, swelling performance, X-ray diffraction

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Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Ali Ulvi Uzer

Abstract:

Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Tatiana Tunon, Gottfried Pestal

Abstract:

Government agencies responsible for the management of fisheries resources publish many types of grey literature, and these materials are increasingly accessible to the public on agency websites. However, scope and quality vary considerably, and end-users need meta-data about the report series when deciding whether to use the information (e.g. apply the methods, include the results in a systematic review), or when prioritizing materials for archiving (e.g. library holdings, reference databases). A proposed taxonomy for these report series was developed based on a review of 41 report series from 6 government agencies in 4 countries (Canada, New Zealand, Scotland, and United States). Each report series was categorized according to multiple criteria describing peer-review process, content, and purpose. A robust classification tree was then fitted to these descriptions, and the resulting taxonomic groups were used to compare agency output from 4 countries using reports available in their online repositories.

Keywords: classification tree, fisheries, government, grey literature

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Authors: Tohid Eskandarzadeh, Nader Bahlouli, Turaj Behnam, Azra Jafarzadeh

Abstract:

The purpose of the study reported in this paper was to propose an optimal pattern to evaluate the staff management performance of the water and sewage organization. The performance prism-model was used to evaluate the following significant dimensions of performance: organizational strategies, organizational processes, organization capabilities, stakeholders’ partnership and satisfaction. In the present study, a standard, valid and reliable questionnaire was used to obtain data about the five dimensions of the performance prism model. 169 sample respondents were used for responding the questionnaire who were selected from the staff of water and waste-water organization in western Azerbaijan, Iran. Also, Alpha coefficient was used to check the reliability of the data-collection instrument which was measured to be beyond 0.7. The obtained data were statistically analyzed by means of SPSS version 18. The results obtained from the data analysis indicated that the performance of the staff management of the water and waste-water organization in western Azerbaijan was acceptable in terms of organizational strategies, organizational process, stakeholders’ partnership and satisfaction. Nevertheless, it was found that the performance of the staff management with respect to organizational abilities was average. Indeed, the researchers drew the conclusion that the current performance of the staff management in this organization in western Azerbaijan was less than ideal performance.

Keywords: performance evaluation, performance prism model, water, waste-water organization

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

Abstract:

The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

Abstract:

In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: Vidya Pratap, Seena Biju, Keshavdev A.

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In the educational town of Manipal (located in southern India) households dispose their wastes without segregation. Mixed wastes (organic, inorganic and hazardous items) are collected either by private collectors or by the local municipal body in trucks and taken to dump yards. These collectors select certain recyclables from the collected trash and sell them to scrap merchants to earn some extra money. Rag pickers play a major role in picking up card board boxes, glass bottles and milk sachets from dump yards and public areas and scrap iron from construction sites for recycling. In keeping with the Indian Prime Minister’s mission of Swachh Bharat (A Clean India), the local municipal administration is taking efforts to ensure segregation of domestic waste at source. With this in mind, each household in a residential area in Manipal was given two buckets – for wet and dry wastes (wet waste referred to organic waste while dry waste included recyclable and hazardous items). A study was conducted in this locality covering a cluster of 145 households to assess the residents’ knowledge of recyclable, organic and hazardous items commonly disposed by households. Another objective of this research was to evaluate the extent to which the residents actually dispose their wastes appropriately. Questionnaires were self-administered to a member of each household with the assistance of individuals speaking the local language whenever needed. Respondents’ knowledge of whether an item was organic, inorganic or hazardous was captured through a questionnaire containing a list of 50 common items. Their behaviour was captured by asking how they disposed these items. Results show that more than 70% of respondents are aware that banana and orange peels, potato skin, egg shells and dried leaves are organic; similarly, more than 70% of them consider newspapers, notebook and printed paper are recyclable. Less than 65% of respondents are aware that plastic bags and covers and plastic bottles are recyclable. However, the results of the respondents’ recycling behaviour is less impressive. Fewer than 35% of respondents recycle card board boxes, milk sachets and glass bottles. Unfortunately, since plastic items like plastic bags and covers and plastic bottles are not accepted by scrap merchants, they are not recycled. This study shows that the local municipal authorities must find ways to recycle plastic into products, alternate fuel etc.

Keywords: behaviour, knowledge, plastic waste management, recyclables

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Authors: Nnadi Ezekiel Ejiofor

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In Enugu, Nigeria, this study investigates the connection between strategic leadership and project management sustainability, with an emphasis on building projects in the State. The study set out to accomplish two specific goals: first, it sought to establish a link between creative project management and resource efficiency in construction projects in Enugu State, Nigeria; and second, it sought to establish a link between innovative thinking and waste minimization in those same projects. A structured questionnaire was used to collect primary data from 45 registered construction enterprises in the study area as part of the study's descriptive research approach. Due to the nonparametric nature of the data, Spearman Rank Order Correlation was used to evaluate the acquired data. The findings demonstrate that creative project management had a significant positive impact on resource efficiency in construction projects carried out by architecture firms in Enugu State, Nigeria (r =.849; p.001), and that innovative thinking had a significant impact on waste reduction in those same projects (r =.849; p.001). It was determined that strategic leadership had a significant impact on the sustainability of project management, and it was thus advised that project managers should foresee, prepare for, and effectively communicate present and future developments to project staff in order to ensure that the objective of sustainable initiatives, such as recycling and reuse, is implemented in construction projects.

Keywords: construction, project management, strategic leadership, sustainability, waste reduction

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Authors: Gizem Eser Erdek

Abstract:

This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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