Search results for: vegetation classification

Authors: Kanchan J. Kakade, S. A. Annadate

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This paper presents design and development of underground coal mine monitoring using mbed arm cortex controller and ZigBee communication. Coal mine is a special type of mine which is dangerous in nature. Safety is the most important feature of a coal industry for proper functioning. It’s not only for employees and workers but also for environment and nation. Many coal producing countries in the world face phenomenal frequently occurred accidents in coal mines viz, gas explosion, flood, and fire breaking out during coal mines exploitation. Thus, such emissions of various gases from coal mines are necessary to detect with the help of robot. Coal is a combustible, sedimentary, organic rock, which is made up of mainly carbon, hydrogen and oxygen. Coal Mine Detection Robot mainly detects mash gas and carbon monoxide. The mash gas is the kind of the mixed gas which mainly make up of methane in the underground of the coal mine shaft, and sometimes it abbreviate to methane. It is formed from vegetation, which has been fused between other rock layers and altered by the combined effects of heat and pressure over millions of years to form coal beds. Coal has many important uses worldwide. The most significant uses of coal are in electricity generation, steel production, cement manufacturing and as a liquid fuel.

Keywords: Zigbee communication, various sensors, hazardous gases, mbed arm cortex M3 core controller

Procedia PDF Downloads 466

Authors: Rosario Rivera Rubite, Ching-I Peng, Che-Wei Lin, Mark Hughes, Yoshiko Kono, Kuo-Fang Chung

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The flora of Panay Island is under-collected compared with the other islands of the Philippines. In a joint expedition to the island, botanists from Taiwan and the Philippines found three unknown Begonia and compared them with potentially allied species. The three species are clearly assignable to Begonia section Baryandra which is largely endemic to the Philippines. Studies of literature, herbarium specimens, and living plants support the recognition of the three new species: Begonia culasiensis, Begonia merrilliana, and Begonia sykakiengii. Somatic chromosomes at metaphase were determined to be 2n=30 for B. culasiensis and 2n=28 for both B. merrilliana and B. sykakiengii, which are congruent with those of most species in sect. Baryandra. Molecular phylogenetic evidence is consistent with B. culasiensis being a relict from the late Miocene, and with B. merrilliana and B. sykakiengii being younger species of Pleistocene origin. The continuing discovery of endemic Philippine species means the remaining fragments of both primary and secondary native vegetation in the archipelago are of increasing value in terms of natural capital. A secure future for the species could be realized through ex-situ conservation collections and raising awareness with community groups.

Keywords: conservation, endemic , herbarium , limestone, phylogenetics, taxonomy

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

Procedia PDF Downloads 127

Authors: Semire Yekta

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Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

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Authors: Hala M. El-hanbuli, Mohammed F. Darweesh

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The incidence rates of any tumor vary hugely with geographical location. Basal Cell Carcinoma (BCC) is one of the most common skin cancer that has many histopathologic subtypes. Objective: The aim was to study the histopathological features of BCC cases that were received in the Pathology Department, Kasr El-Aini hospital, Cairo University, Egypt during the period from Jan 2004 to Dec 2013 and to evaluate the clinical characters through the patient data available in the request sheets. Methods: Slides and data of BCC cases were collected from the archives of the pathology department, Kasr El-Aini hospital. Revision of all available slides and histological classification of BCC according to WHO (2006) was done. Results: A total number of 310 cases of BCC representing about 65% from the total number of malignant skin tumors examined during the 10-years duration in the department. The age ranged from 8 to 84 years, the mean age was (55.7 ± 15.5). Most of the patients (85%) were above the age of 40 years. There was a slight male predominance (55%). Ulcerated BCC was the most common gross picture (60%), followed by nodular lesion (30%) and finally the ulcerated nodule (10%). Most of the lesions situated in the high-risk sites (77%) where the nose was the most common site (35%) followed by the periocular area (22%), then periauricular (15%) and finally perioral (5%). No lesion was reported outside the head. The tumor size was less than 2 centimeters in 65% of cases, and from 2-5 centimeters in the lesions' greatest dimension in the rest of cases. Histopathological reclassification revealed that the nodular BCC was the most common (68%) followed by the pigmented nodular (18.75%). The histologic high-risk groups represented (7.5%) about half of them (3.75%) being basosquamous carcinoma. The total incidence for multiple BCC and 2nd primary was 12%. Recurrent BCC represented 8%. All of the recurrent lesions of BCC belonged to the histologic high-risk group. Conclusion: Basal Cell Carcinoma is the most common skin cancer in the 10-year survey. Histopathological diagnosis and classification of BCC cases are essential for the determination of the tumor type and its biological behavior.

Keywords: basal cell carcinoma, high risk, histopathological features, statistical analysis

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Kisik Song, Sungjoo Lee

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

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

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Authors: Cenkmen R. Begburs

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In Antalya region, freshwater shrimps are usually collected with scoops and tin traps. However, it can be catched by reef traps in some water sources. Freshwater shrimps are constantly catching for commercial reasons because of a favorite bait for angling. There are more or less damage catching fishing vehicles to the habitat. This study was carried out in the Kırkgöz spring, Antalya and examined the effect of reef traps on the Kırkgöz spring habitat. Reef traps used 18.5x23.5x25 cm perforated bricks are arranged next to each other, blocks of random dimensions are prepared in 5x10, 18x24, 7x8 meter dimensions. These blocks are constructed with two layers of bricks that are covered with various materials such as carpets and blankets. Then, freshwater shrimps enter the holes of bricks. The bricks are closed off from both sides and discharged into a container when it is desired to be caught. The reef traps built on the plants which staying on the plant for a long time have been damaging the vegetation under the reef traps. Fishermen are setting new traps on the plants to increase the fishing efficiency since the freshwater shrimps are among the water plants. As a result, this application disrupts the aquatic organisms in their habitats. It is important to use fishing gears which will cause less damage and conserve stocks for sustainable fishing.

Keywords: reef trap, Antalya, environment, damage

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Authors: George Zhou, Yunchan Chen, Candace Chien

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Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

Procedia PDF Downloads 336

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Guoheng Liu, Zhilong Huang

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For more than the past ten years, oil and gas production from marine shale such as the Barnett shale. In addition, in recent years, major breakthroughs have also been made in lacustrine shale gas exploration, such as the Yanchang Formation of the Ordos Basin in China. Lucaogou Formation shale, which is also lacustrine shale, has also yielded a high production in recent years, for wells such as M1, M6, and ML2, yielding a daily oil production of 5.6 tons, 37.4 tons and 13.56 tons, respectively. Lithologic identification and classification of reservoirs are the base and keys to oil and gas exploration. Lithology and lithofacies obviously control the distribution of oil and gas in lithological reservoirs, so it is of great significance to describe characteristics of lithology and lithofacies of reservoirs finely. Lithofacies is an intrinsic property of rock formed under certain conditions of sedimentation. Fine-grained sedimentary rocks such as shale formed under different sedimentary conditions display great particularity and distinctiveness. Hence, to our best knowledge, no constant and unified criteria and methods exist for fine-grained sedimentary rocks regarding lithofacies definition and classification. Consequently, multi-parameters and multi-disciplines are necessary. A series of qualitative descriptions and quantitative analysis were used to figure out the lithofacies characteristics and its effect on oil accumulation of Lucaogou formation fine-grained sedimentary rocks in Santanghu basin. The qualitative description includes core description, petrographic thin section observation, fluorescent thin-section observation, cathode luminescence observation and scanning electron microscope observation. The quantitative analyses include X-ray diffraction, total organic content analysis, ROCK-EVAL.II Methodology, soxhlet extraction, porosity and permeability analysis and oil saturation analysis. Three types of lithofacies were mainly well-developed in this study area, which is organic-rich massive shale lithofacies, organic-rich laminated and cloddy hybrid sedimentary lithofacies and organic-lean massive carbonate lithofacies. Organic-rich massive shale lithofacies mainly include massive shale and tuffaceous shale, of which quartz and clay minerals are the major components. Organic-rich laminated and cloddy hybrid sedimentary lithofacies contain lamina and cloddy structure. Rocks from this lithofacies chiefly consist of dolomite and quartz. Organic-lean massive carbonate lithofacies mainly contains massive bedding fine-grained carbonate rocks, of which fine-grained dolomite accounts for the main part. Organic-rich massive shale lithofacies contain the highest content of free hydrocarbon and solid organic matter. Moreover, more pores were developed in organic-rich massive shale lithofacies. Organic-lean massive carbonate lithofacies contain the lowest content solid organic matter and develop the least amount of pores. Organic-rich laminated and cloddy hybrid sedimentary lithofacies develop the largest number of cracks and fractures. To sum up, organic-rich massive shale lithofacies is the most favorable type of lithofacies. Organic-lean massive carbonate lithofacies is impossible for large scale oil accumulation.

Keywords: lithofacies classification, tuffaceous shale, oil enrichment, Lucaogou formation

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Authors: N. Tochukwu, M. Mukhopadhyay, A. Mohamed

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It is no new that Nigeria faces a continual power shortage problem due to its vast population power demand and heavy reliance on nonrenewable forms of energy such as thermal power or fossil fuel. Many researchers have recommended using geothermal energy as an alternative; however, Past studies focus on the geophysical & geochemical investigation of this energy in the sedimentary and basement complex; only a few studies incorporated the remote sensing methods. Therefore, in this study, the preliminary examination of geothermal resources in the Middle Benue was carried out using satellite imagery in ArcMap. Landsat 8 scene (TIR, NIR, Red spectral bands) was used to estimate the Land Surface Temperature (LST). The Maximum Likelihood Classification (MLC) technique was used to classify sites with very low, low, moderate, and high LST. The intermediate and high classification happens to be possible geothermal zones, and they occupy 49% of the study area (38077km2). Riverline were superimposed on the LST layer, and the identification tool was used to locate high temperate sites. Streams that overlap on the selected sites were regarded as geothermal springs as. Surprisingly, the LST results show lower temperatures (<36°C) at the famous thermal springs (Awe & Wukari) than some unknown rivers/streams found in Kwande (38°C), Ussa, (38°C), Gwer East (37°C), Yola Cross & Ogoja (36°C). Studies have revealed that temperature increases with depth. However, this result shows excellent geothermal resources potential as it is expected to exceed the minimum geothermal gradient of 25.47 with an increase in depth. Therefore, further investigation is required to estimate the depth of the causative body, geothermal gradients, and the sustainability of the reservoirs by geophysical and field exploration. This method has proven to be cost-effective in locating geothermal resources in the study area. Consequently, the same procedure is recommended to be applied in other regions of the Precambrian basement complex and the sedimentary basins in Nigeria to save a preliminary field survey cost.

Keywords: ArcMap, geothermal resources, Landsat 8, LST, thermal springs, MLC

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Authors: Pennapa Palapin

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This study aimed to examine the management and development of forest tourism Kamchanoad. Ban Dung, Udon Thani sustainability. Data were collected by means of qualitative research including in-depth interviews, semi-structured, and then the data were summarized and discussed in accordance with the objectives. And make a presentation in the form of lectures. The target population for the study consisted of 16 people, including representatives from government agencies, community leaders and the community. The results showed that Guidelines for the Management and Development of Forest Tourism Kamchanoad include management of buildings and infrastructure such as roads, water, electricity, toilets. Other developments are the establishment of a service center that provides information and resources to facilitate tourists.; nature trails and informative signage to educate visitors on the path to the jungle Kamchanoad; forest activities for tourists who are interested only in occasional educational activities such as vegetation, etc.; disseminating information on various aspects of tourism through various channels in both Thailand and English, as well as a website to encourage community involvement in the planning and management of tourism together with the care and preservation of natural resources and preserving the local cultural tourist area of Kamchanoad.

Keywords: guidelines for the management and development, forest tourism, Kamchanoad, sustainability

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

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The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Anjan Naidu

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In this paper we first discuss the minimum spanning tree, then we use the Kruskal algorithm to obtain minimum spanning tree. Based on Kruskal algorithm we propose Kruskal algorithm to apply an application to find minimum cost applying the concept of spanning tree.

Keywords: Minimum Spanning tree, algorithm, Heuxistic, application, classification of Sub 97K90

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Authors: Ahlem Bouziri, Chiraz Latiri, Eric Gaussier

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. The key feature of our approach is a better trade off between the size of the mining result and the conveyed knowledge. Thus, face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm such as SVM or k-means. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results. The experiments were performed on SDA 95 collection, a data collection for information retrieval. It was found that the results were better in both terms of MAP and NDCG. The main observation is that the hybridization of text mining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

Keywords: supervised leaning, classification, query expansion, association rules

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Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

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A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

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Authors: Munruchi Kaur

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Agarics are the fruiting bodies of the fungi falling under Phylum Basidiomycota of class Agaricomycetes. North Western parts of India which comprises of mighty Himalayas decorated with snow cap mountains, forested areas, grassland and the Gangetic plains with the altitude varying between 196m to 3600m have a huge potential of naturally growing wild agarics. These mushrooms lavishly grow in wet humid weather conditions that prevail in these parts of India during the monsoon which hits in the early June and continue up to mid-October. In this area, a diverse form of mixed vegetation is available which is represented by coniferous and angiospermic trees, shrubs, herbs, epiphytes, parasites, climbers etc. The vegetation, topography and climate of this area is quite favorable for the growth of agarics. Cedrus deodara, Pinus longifolia, P. roxburghii, P. wallichiana, Abies pindrow, A. spectabilis, Picea smithiana, Taxus sp., Rhododendron sp. and Quercus sp. occur in pure formations or as scattered patches or as mixed forests, whereas the Gangetic plains are dominated by the angiospermic trees and shrubs, they commonly occur along roadsides or in conserved areas or are the avenues plantations, common amongst these are Shorea robusta, Dalbergia sissoo, Melia azadirachta, Acacia sp., Ficus benghalensis, Eucalyptus sp. and Butea monosperma. These agarics can be categorized on the basis of the habitat in which they grow they are usually foliocolous, lignicolous, humicolous, coprophilous or termitophilous. A number of fungal forays were undertaken to different parts of North West India from time to time during the monsoon season with an aim to decipher the agarics diversity of this part of India. Along with collecting the various agarics from diverse habitat, the ethnomycological data was also collected along with by interacting with the local inhabitants of those areas. Based upon the ethnomycological data collected over the years, cataloging of the edible and inedible agarics has been done and cultures of such potential edible agarics were raised with an aim to domesticate these selected taxa. With an aim to reduce the local pressure on these natural resources, a low-cost technology was developed to make it available to the public for cultivation. As a result, 104 taxa were found edible such as Amanita hemibapha var. ochracea, A. chepangiana, A. banningiana, A. vaginata, Agrocybe parasitica, Author: Professor & Dean Faculty of Life Sciences Punjabi University, Patiala. Punjab, India [email protected] Agaricus bisporus, A. andrewii, A. campestris var. campestris, A. silvicola, A. subrutilescens, A. bernardii, A. abruptibulbus, A. fuscovelatus, A. brunnescens, A. augustus, A. silvaticus, A. arvensis, Volvariella bakeri, V. terastia, V. bombycina, V. diplasia, Psathyrella candolleana, Volvopluteus gloiocephalus, Russula cyanoxantha, R. atropurpurea, R. aurea, Clitocybe gibba,Lentinus transitus, L. kashmirinus, L. crinitus, L. ligrinus, Lactarius rubrilacteus, Pleurotus sapidus, Pluteus subcervinus, Macrocybe gigantea, etc. Cultures of various taxa viz. Pleurotus sajor-caju, Macrocybe gigantea, Pluteus petasatus and Lentinus tigrinus were raised and a proper protocol for the domestication of Pleurotus sajor-caju, Macrocybe gigantea, and Lentinus tigrinus has been developed using the locally available agro-wastes.

Keywords: Agaric, culture, domestication, edible

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Ashraf M. S. Soliman, Mohsen Elhasadi

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The present study was undertaken to evaluate the impact of ambient particles emitted from an industrial complex located at El-Fatyah on growth, phytomass partitioning and accumulation, pigment content and nutrient uptake of two economically important crop species; barley (Hordeum vulgare L.Family: Poaceae) and broad bean (Vicia faba L. Family: Fabaceae) growing in the region. It was obvious from the present investigation that chlorophyll and carotenoid content showed significant responses to the industrial dust. Generally, the total pigment content of the two investigated crops in the two locations continually increased till the plant age reached 70 days after sowing then begins to decrease till the end of the growing season..The total uptake of N, P and K in the two studied species decreased in response to industrial dust in the study area compared to control location. In conclusion, barley and broad bean are very sensitive to air pollutants, and may consider as bioindicators for atmospheric pollution. Pollutants caused damage of their leaves, impair plant growth, hindered nutrient uptake and consequently limit primary productivity.

Keywords: Effect of Industrial Complex on barley and broad bean

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Minakshi Jain, I. P. Singh

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Due to the era of outstanding technology and modern lifestyle, human beings are debasing their physical, psychological, and mental well-being. The effect of this leads to a trail of chronic diseases like anxiety, loneliness, and depression, especially in children and young adults. This is visible in individuals who suffer from clinical depression, which leads to impaired mood and distortion of cognition, particularly in children. The Members of the WHO European Region made a declaration to provisioning each child with access to healthy and safe environments by 2020, and the decision was taken at the Conference on Environment and Health in 2010 as an initiative to improve access to green spaces in cities which provides universal access for quality spaces for both social interaction and human well-being. In line with this, the paper aims to establish a prospective study on linking green spaces and CBT (Cognitive behavior therapy) in order to treat disorders with reference to children and young adults. A questionnaire was adopted to explore the possibility of green spaces as additive measures for the existing modes of therapy. The results adapted from the questionnaire show that certain species of vegetation have a significant effect in enhancing effective mental well-being.

Keywords: CBT, therapeutic gardens UCLA loneliness scale, anxiety, depression, green spaces, biophilia, environmental psychology

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Authors: Hanan M. Taleb

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Since buildings are a major consumer of energy, their potential impact on the environment is considerable. Therefore, expanding the application of low energy architecture is of the utmost importance. Designing with nature is also one of the most attractive methods of design for many architects and designers because it creates a pathway to sustainability. One feature of designing with nature is the use of green roofing which aims to cover the roof with vegetation either partially or completely. Appreciably, green roofing in a building has many advantages including absorbing rainwater, providing thermal insulation, enhancing the ecology, creating a peaceful retreat for people and animals, improving air quality and helping to offset the air temperature and heat island effect. The aim of this paper is to monitor energy saving in the residential buildings of Dubai after applying green roofing techniques. The paper also attempts to provide a thermal analysis after the application of green roofs. A villa in Dubai was chosen as a case study. With the aid of energy simulation software, namely Design Builder, as well as manual recording and calculations, the energy savings after applying the green roofing were detected. To that extent, the paper draws some recommendations with regard to the types of green roofing that should be used in these particular climatic conditions based on this real experiment that took place over a one year period.

Keywords: residential buildings, Dubai, energy saving, green roofing, CFD, thermal comfort

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