Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis for Dengue Fever Using Spatial Data Mining

— The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a given data set. This enable to extract pattern from spatial database using k-means algorithm which refers to patterns not explicitly stored in spatial databases. Since spatial Association mining needs to evaluate multiple spatial relationships among a large number of spatial objects. An interesting mining optimization method called progressive refinement can be adopted in spatial association analysis. The method first mines large data sets roughly using a fast algorithm and then improves the quality of mining in a pruned data set. The k-means algorithm randomly selects k number of objects, each of which initially represents a cluster mean or center. For each of the remaining objects, an object is assigned to the cluster to which it is most similar, based on the distance between the object and the cluster mean. Then it computes new mean for each cluster. This process iterates until the criterion function converges. The above concept is applied in the area of image segmentation where to apply the microscopic blood image as input and signals are filtered with the help of neural network to predict the best result about dengue fever.
Knowledge and practice of pain management among nurses in labour wards in Ibadan, Nigeria

Background: It is not clear whether awareness of pain management has influenced the management of labour pain by nurse/midwives in Ibadan, Nigeria. Many women in labour do not get optimal pain relief, despite awareness of the importance of pain management. Aims: This study aimed to assess knowledge and practice of labour pain management among nurse/midwives in Ibadan. Methods: This cross-sectional study included 87 nurse/midwives on duty in labour rooms of three hospitals in Ibadan, Nigeria. Data were collected using semi-structured questionnaires. Descriptive statistics, and Pearson's and Kendall's correlation coefficients were used for data analysis. All analyses were performed using IBM SPSS version 20.0 at a 5% level of significance. Findings: Two thirds of respondents (66.7%) had a moderate level of knowledge, and 34.5% managed labour pain through back massage/psychotherapy. Knowledge of pain management was associated with level of education (τ = 0.82; P < 0.001) while good practice of pain managemen.
Chemical Analysis of Lipopolysaccharide Extracted From Salmonella entericaserovarTyphi

Lipopolysaccharide (LPS) was extracted from Salmonella entericaserovarTyphi by hot phenol method. SDS-PAGE followed by silver staining of LPS showed a ladder like pattern of LPS molecules with molecular mass ranging from approximately 10 kDa to 55 kDa. Immunoblot of LPS showed that anti-LPS IgG in pooled sera from patients showing a positive Widal reaction reacted with the entire profile of LPS molecules. The extracted LPS was found to contain 50% w/w sugars when analysed for determination of total sugars in the LPS preparation. LPS was cleaved into lipid A and polysaccharide moieties by acetic acid hydrolysis. Thin layer chromatography of lipid A and O-polysaccharide regions showed that polysaccharide moiety had an Rf value of 0.65 while the lipid A moiety had an Rf value of 0.81. The purity of the extracted LPS was found to be similar to the commercial LPS when analysed by high performance liquid chromatography.
An Adaptive Digital Image Watermarking Based on Image Features in Discrete Wavelet Transform Domain and General Regression Neural Network

: In this paper, a new intelligent, robust and adaptive digital watermarking technique for gray image based on combination of discrete wavelet transform (DWT), human visual system (HVS) model and general regression neural network (GRNN) is proposed. Wavelet coefficients are analyzed by a HVS model to select suitable coefficients for embedding the watermark. The watermark bits are extracted from the watermarked image by training a GRNN. Some statistical characteristics of wavelet coefficients are used in extracting process for better performance and accuracy of watermarking algorithm. The implementation results show that the watermarking algorithm has very good robustness to all kind of attacks
Performance Comparison of Neural Networks and GMM for Vocal/Nonvocal segmentation for Singer Identification

— Vocal and nonvocal segmentation is an important task in singing voice signal processing. Before identifying the singer it is necessary to locate the singer’s voice in a song. Maximum of the songs start with a piece of instrumental accompaniment known as ‘prelude’ in musical terms after which the singing voice comes into play. Therefore, it is necessary to detect the vocal region in the song in order to extract the singer’s voice characteristics and to avoid the non-vocal region which includes the instrumental accompaniment. This work thus classifies Vocal and Nonvocal region in songs using three different classifiers: Gaussian Mixture Model (GMM), Artificial Neural Network (ANN) with Feed Forward Backpropagation algorithm and Learning Vector Quantization (LVQ). Mel Frequency Cepstral Coefficient (MFCC) has been considered as the primary feature for classification. An available database MUSCONTENT is used and a newly created Database ASDB1 consisting of sixty excerpts from a wide variety of Assamese songs has been examined applying the same methods of classification. The efficacy of the classifiers has been tested and the results indicate that LVQ is a robust classifier compared to FFBP and GMM.
A Temporal Logic Based Approach to Multi-Agent Intrusion Detection and Prevention

Collaborative systems research in the last decade have led to the development in several areas ranging from social computing, e-learning systems to management of complex computer networks. Intrusion Detection Systems (IDS) available today have a number of problems that limit their configurability, scalability or efficiency. An important shortcoming is that the existing architectures is built around a single entity that does most of the data collection and analysis. This work introduces a new architecture for intrusion detection and prevention based on multiple autonomous agents working collectively. We adopt a temporal logic approach to signature-based intrusion detection. We specify intrusion patterns as formulas in a monitorable logic called EAGLE. We also incorporate logics of knowledge into the agents. We implement a prototype tool, called MIDTL and use this tool to detect a variety of security attacks in large log-files provided by DARPA.
A Definitional Analysis with Implications for Classroom Teaching

Pragmatics has been a field that has been considered by some scholars to be rather difficult to define and/ or not sufficiently important to be considered distinct to semantics. It has also not always received sufficient attention in L2 learning. A clear definition of what pragmatics is in terms of context and meeting truth conditions is presented as well the potential for ambiguity in defining cross-cultural pragmatic failure from other types. The analysis demonstrates the importance of such understandings of pragmatics and some of the implications for teaching it particularly in the L2 classroom, so that students can be better equipped to avoid cross-cultural communication problems. Based on Jung's seminal work at, there are also a number of insightful ideas on how acquisitorial pragmatics can be approached. One important area of focus relates to ensuring L2 learners gain an acceptable appreciation of pragmatic strategies of relevance. Again understanding the pragmatics of politeness and face saving is an important area to return to for L2 students to get right in their language learning strategies. For consistent to Hanza getting a grasp on what constitutes cross-culture failure through losing face due to low pragmatic competence would seem to be especially worthy of classroom attention. For example, though a milder example of impoliteness, for some, certain L2 speakers may not understand the differences of how and when to use such modals as “can” and “could” versus the conditional “would”; the latter of which carries a more imperative meaning than the two modals in respect to making requests -at least among middle class Americans.
Secure Semantic Multi-keyword Synonym Ranked Query over Encrypted Cloud Data

--Cloud computing is a new auspicious technology and it greatly accelerates the development of large-scale data storage, computing and distribution. With the benefit of cloud computing, data owners are driven to outsource their penetrating and complex data from local machines to the public cloud due cost, efficiency and less hands on management. However, security and privacy become major security issues when data owners outsource their private data onto untrusted public cloud servers. Hence, to protect data, penetrating data has to be encrypted before outsource onto the cloud system. However, traditional keyword plain text search is obsolete. Hence, achieving encrypted cloud data search is utmost important. Existing searching techniques over encrypted cloud data considers only exact or fuzzy keyword or multi-keyword, but not semantic and synonym based ranked searching using multi-keyword. Hence, how to facilitate an effective searchable system to support semantic and synonym query is still challenging issue. This paper defines and proposes an effective solution to solve the problem of semantic multi-keyword synonym query over encrypted cloud data by retrieving top k scored documents, termed as SMSRQE. In this SMSRQE, keywords and corresponding synonyms are mapped to design multi-keyword synonym dictionary. Hence, it gives accurate search results for data user interested keywords or synonyms or both. To find semantically related documents, we used Latent Semantic Index (LSI). Our experiments show the SMSRQE methodology gives effective, efficient and accurate search results over the current solutions.
Efflux from Temperature Sensitive Liposomes and In Situ Formation of Metal Cholate Liposome Gels: Basic Studies and Potentials for Sustained Site-Specific Drug Delivery

Liposomes or vesicles are biomimetic close containers for the delivery of drugs at the local site for an extended period of time. On the other hand hydrogels which are three-dimensional hydrophilic matrices are another class of popular materials for sustained release. Such hybrids combine the features of liposomes and polymer to ensure a sustained local drug delivery. In the present work a novel liposome/hydrogel soft assembly is explored which may be potentially useful for drug delivery applications. We report thermally triggered release of Ca +2 from temperature sensitive liposomal compartments constituted as 90 mol% DPPC and 10 mol% DMPC to induce rapid gelation of a solution of calcium cholate and AgNO3 (extravesicular precursor fluid). Calcium chloride loaded liposomes were prepared using the lipid film rehydration method. The formation of unilamellar bilayer were supported by the fluorometric studies using a compatible and labeled fluoropore (NBD-PS). Hydrogels were obtained by mixing Ca +2 loaded liposome with extravesicular precursor fluid and incubating the content at 37 o C. The concentration of the cholate during gelation was sublytic in order to avoid vesicle solubilization. The integrity of liposomes within the hydrogels were preserved during gelation as confirmed by transmission electron microscopy (TEM). The presence of low concentration of cholate (for example 0.05 mM) also permitted spectrophotometric monitoring of Ca +2 efflux for Ca-vesicles employing calcium sensitive dye, Arsenazo III. We expect that this simple experiment may also be useful for developing implantable as well as rapidly gelling injectable biomaterials for site-specific drug delivery. The present work is also significant as the antimicrobial properties of hydrogels containing silver has been widely recognized for its therapeutic profile.
Markovian Model for Packet Loss in MPLS Networks

—In this Paper, we worked on the modeling of packet loss within MPLS environment. The research exploited real data from a real network running MPLS as its core switching. The data were obtained from 6 nodes, each node participated with 6 hours of traffic segments used as data set, all nodes formed 36 hours as testing data. Each dataset was divided into one-hour segment, each segment loss and no-loss was represented as a binary series. From the 36 segments; 26 segments were found stationary. For Stationary segments;9 segments were modeled using Bernoulli model, 16 segments were modeled using 2-state Markov chain and 9 segments showed k-th order Markov chain tendencies with orders higher than 2. these segments were excluded from modeling due to the uniqueness of their conditions. 4 other segments were found non-stationary thus couldn't be modeled. Each segment stationarity was checked using average filter conditions with threshold of 0.05 and with confidence of 0.95 for lags selection. After modeling each segment independently; an average loss model for each node was estimated using its modeled segments. confidence of these models were 95%. The Model error was tested and the error was around 0.001 for a segment.
Design and Evaluation of a Novel Social Perception System for Human-Robot Interaction

: Robot‟s perception is essential for performing high-level tasks such as understanding, learning, and in general, human-robot interaction (HRI). For this reason, different perception systems have been proposed for different robotic platforms in order to detect high-level features such as facial expressions and body gestures. However, due to the variety of robotics software architectures and hardware platforms, these highly customized solutions are hardly interchangeable and adaptable to different HRI contexts. In addition, most of the developed systems have one issue in common: they detect features without awareness of the real-world contexts (e.g., detection of environmental sound assuming that it belongs to a person who is speaking, or treating a face printed on a sheet of paper as belonging to a real subject). This paper presents a novel social perception system (SPS) that has been designed to address the previous issues. SPS is an out-of-the-box system that can be integrated into different robotic platforms irrespective of hardware and software specifications. SPS detects tracks and delivers in real-time to robots, a wide range of human-and environment-relevant features with the awareness of their real-world contexts. We tested SPS in a typical scenario of HRI for the following purposes: to demonstrate the system capability in detecting several high level perceptual features as well as to test the system capability to be integrated into different robotics platforms. Results show the promising capability of the system in perceiving real world in different social robotics platforms, as tested in two humanoid robots i.e., FACE and ZENO.
Moving Object Tracking Using Gaussian Kernel and Template Modelling In High Resolution Remote Sensing Imagery

: Object Tracking is used to associate target objects in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the frame rate. This paper deals with object tracking from videos based on template matching using gaussian kernel with probability distribution function and mean shift algorithm. Initially, the target will be selected from chosen video sequence to track desired object in consecutive frames. The target will be utilized to determine the probability distribution function for similarity measurement between target and current processing frames. Here, gaussian kernel function and its gradient are used to find the PDF for corresponding templates. Similarity between two different images will be measured by weighted sum of gaussian coefficients and PDFs. Mean shift approach used here to shift the starting coordinates of template to find its similar features in consecutive frames to detect the desired object. The dissimilarity between the target model and target candidates will be expressed by a metric derived from Bhattacharyya coefficient. The project simulated results shows that moving object from video will be tracked accurately at different position and shape with help of templates in a considerable amount of time.
GENETIC VARIATION IN SELECTED SPECIES OF PRAWNS OF GENUS MACROBRACHIUM USING MITOCHONDRIAL 16S rRNA

Mitochondrial partial 16s ribosomal gene was used to infer the genetic variation of species of genus Macrobrachium. From 481 bases, the number of variable sites for Macrobrachium rosenbergii is 236, Macrobrachium villosimanus is 34, Macrobrachium lar is 119 and Macrobrachium idella is 105. The average nucleotide frequencies were A: 35.1%; T: 29.8%; C: 22.4%, G: 12.6% and the A+T rich sequences was 64.9%. The Ts/Tv ratio is 27. More number of haplotypes was seen in Macrobrachium rosenbergii population. Neutrality index Tajima’s D value of Macrobrachium rosenbergii population was negative. High haplotype and low to moderate nucleotide diversity were seen for all the populations of different species and the populations are expanding in size.
Tracing the effect of pretilachlor on Channa punctatus (Bloch)

Pesticides usage is increasing worldwide with the increase in human population. These pesticides are mostly with long half life for better efficiency. Most of the pesticides are drained to the nearby water-bodies by run off or leaching etc. After reaching aquatic system these are accumulated within the aquatic organisms. Bioaccumulation in fish is very common and ultimately threatening to the associated food web especially to the fish eating human community. Channa punctatus is a common fish prevalent in different water bodies throughout various parts of India. In this study attempts have been made to understand the possible effect of a common herbicide pretilachlor on the physicochemical characteristics of treated water as well as on the various physiological and anatomical characteristics of Channa punctatus. Various alterations have been noted in these parameters which are predominantly related to the pesticide itself. Furthermore data generated through this study could be useful to assess environment risk in terms of pesticide pollution in aquatic system.
A Survey on Generation and Evolution of Various Cryptographic Techniques

- The evolution of wireless transmission in this modern era, daily touches the new heights of technology. There are so many eyes on the data transmitted over the network. This flow of data over the network requires some sort of security in terms of confidentiality, secrecy, authenticity etc. cryptography is one of the techniques used to provide security to the data flowing over the network by encryption and decryption. Till now, several cryptosystems have been designed and still they are introducing. So far, a large number of encryption/decryption techniques have been implemented and proposed. In this paper we have surveyed some symmetric key cryptographic techniques; we made study of analysis and basic comparison among them. This paper lists some basic features, advantages, disadvantages of various symmetric cryptographic algorithms.
PLANT DISEASE DETECTION AND CLASSIFICATION USING IMAGE PROCESSING AND ARTIFICIAL NEURAL NETWORKS

- Agricultural productivity is something on which Indian economy highly depends. This is one of the reasons that disease detection in plants plays an important role in agricultural field, as having disease in plants is quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is deteriorated. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops
Stress and Other Factors Involved in the Pathophysiology of Irritable Bowel Syndrome. Review

Stress is associated with gastrointestinal diseases. Irritable bowel syndrome (IBS) is a common multifactorial functional gastrointestinal disorder with a point prevalence of 11% in the adult western population. However, it can reach 35% depending on the country. It is has observed a higher prevalence of functional digestive disorders in certain families which has established the possibility of a genetic predisposition the variability in prevalence can be due to different symptom-based diagnostic criteria, sample selection, access to health care, and/or cultural factors. Authors have demonstrated the role of negative affects, including depression, anxiety, and anger, on pathogenesis and clinical manifestations of IBS. In inpatients with psychological stress have been demonstrated increased rates of IBS and dyspepsia. The etiopathogenesis of IBS not yet completely understood. This pathology is characterized by: abnormal colonic motility with alterations in bowel habits (constipation and/or diarrhea), altered stool passage (urgency or feeling of incomplete evacuation), visceral hypersensitivity, perception of pain incremented and feelings of abdominal distension, alimentary intolerance, participation of mast cells and inflammation, alteration of the intestinal microbiota, affectation of the autonomic nervous system, and by the influence of the stress in its development and evolution. The autonomic nervous system, stress-hormone system, and immune system have an important role in the pathophysiology of IBS. It is know that stressors that affect and alter the hypothalamic-pituitary-adrenal and the intestine-brain axis induce the secretion of hormones, cytokines or regulating proteins and other mediators. These make up a complex network of interactions with an important dysregulation, affectation of the gastrointestinal tract, recurrent clinic manifestations, and propensity to the chronicity and to the deterioration of the quality of life. However, there are substantial differences in the response each individual to acute or chronic stress. Therefore, is obvious to evaluate the organic and psychological consequences of individuals under stress with IBS, for the prescription of timely therapy.
ESTIMATION OF REVIEW HELPFULNESS BY CONTENT COVERAGE AND WRITING STYLE

Customer reviews are helpful information to decide what to buy in EC sites. However, some customer reviews are not helpful. Therefore, users must choose helpful ones. This is difficult especially for users who do not have enough knowledge about the products. In this paper, we propose methods to classify customer reviews into helpful and unhelpful. To classify them, we focus on content coverage and writing style of reviews. Coverage expresses how many important words are contained by the reviews. Writing style is expressed as “formal” or “informal”, and it is classified by a machine learning technique. We made test data from user votes in Amazon.co.jp, and evaluated our methods. The accuracy of our rule-based method was 0.69. However, the method using coverage does not work well when many reviews have not been written yet. We analyzed the effects of the number of reviews, and confirmed that our rule-based method is less affected and achieves better accuracy than the method using only coverage.