Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things for Environmental Monitoring

-Increasing population in cities demands satisfactory provision of services and infrastructure for the city‘s residents and visitors. The deployment of information and communication technologies to accomplish this objective presents an opportunity for the progress of cities, where management of city and citizens are given access to real-time information about the environment. This paper presents the structure for environmental monitoring using the Internet of Things (IoT). The structure comprises the complete information system from the sensor level to data management and cloud-based information about the environment. This Iot vision is applied to temperate, waste management and vehicle parking to demonstrate a method for existing systems that can be adopted for the enhancement and delivery of services.
Pediatric shock

10.2217/14750708.5. 4.405 © 2 Pediatric shock accounts for significant mortality and morbidity worldwide, but remains incompletely understood in many ways, even today. Despite varied etiologies, the end result of pediatric shock is a state of energy failure and inadequate supply to meet the metabolic demands of the body. Although the mortality rate of septic shock is decreasing, the severity is on the rise. Changing epidemiology due to effective eradication programs has brought in new microorganisms. In the past, adult criteria had been used for the diagnosis and management of septic shock in pediatrics. These have been modified in recent times to suit the pediatric and neonatal population. In this article we review the pathophysiology, epidemiology and recent guidelines in the management of pediatric shock.
Impact of top management support , knowledge management and work place spirituality on performance : A case study of universities Rawalpindi and Islamabad

This study investigates the stochastic behavior of market beta using capital asset pricing model (CAPM) of 50 stocks listed at Karachi Stock Exchange Pakistan from January 1999 to December 2009. This time varying behavior is examined by employing the state space model identified as modified Kalman Filter technique. The static version of CAPM assumes the beta factor to be constant but as the return distribution is time varying in nature thus the beta factor occurs to be non-constant in nature. The stochastic behavior of beta is examined by one of the three dynamic processes: mean reverting process, random coefficient with constant and randomwalk beta. The results indicate that there is large support for the random-walk model for 29 stocks found in the sample random walk behavior for beta in returns. The improvement in fit of timevarying beta over the conventional constant beta estimated by ordinary least square in CAPM model is compared by the coefficient of determination and the variances of the errors. The results of time-varying model show significant gains in terms of a higher explanatory power and lower variance if beta is allowed to follow an appropriate time-varying model. JEL Classification: G12, G15
Comparative Survey on Improved Versions of Apriori Algorithm

— In field of data mining, mining the frequent itemsets from huge amount of data stored in database is an important task. Frequent itemsets leads to formation of association rules. Various methods have been proposed and implemented to improve the efficiency of Apriori algorithm. This paper focuses on comparing the improvements proposed in classical Apriori Algorithm for frequent item set mining.
Ontology Based Data Integration in Federated Databases and Its’ Issues

: In this paper, we discuss the use of ontologies for data integration. We consider two different settings depending on the system architecture: central and peer-to-peer data integration. Within those settings, we discuss five different cases studies that illustrate the use of ontology in metadata representation, in global conceptualization, in high-level querying, in declarative mediation, and in mapping support. Each case study is described in detail and accompanied by examples. Many applications, e.g., data/information fusion, data mining, and decision aids, need to access multiple heterogeneous data sources. These data sources may come from internal and external databases. They have to evolve due to requirement changes. Any change in an application domain induces semantics change in the data sources. The integration of these data sources raises several semantic heterogeneity problems. This has traditionally been the subject of data/schema integration and mapping. However, many heterogeneity conflicts remain in information integration due to lack of semantics. Therefore, richer semantics of data are needed to resolve the heterogeneity problems. Ontological approaches now offer new solution avenues to this interoperability limitation. In this perspective, we propose an ontology based information integration with a local to global ontology mapping as an approach to the integration of heterogeneous data sources. The term “Federated Databases” refers to the data integration of distributed, autonomous and heterogeneous databases. However , a federation can also include information systems, not only databases. At integrating data, several issues must be addressed. Here, we focus on the problem of heterogeneity, more specifically on semantic heterogeneity – that is, problems related to semantically equivalent concepts or semantically related/unrelated concepts. In order to address this problem, we apply the idea of ontologies as a tool for data integration. In this paper, we explain this concept and we briefly describe a method for constructing ontology for Data Integration.
Wavelet Based Classification of Finger Movements Using EEG Signals

- Brain-computer interfaces (BCIs) have been examined in the field of bio-medical engineering. This brain-computer interface method is very useful for the people who are suffered by some nervous disorder to control or operate the external devices. EEG dataset are acquired and these signals are processed for identifying the brain thoughts to control the device. Here we proposed the method for the classification of the finger movements using EEG signals which are used in the application of artificial upper limb. This method includes pre processing, feature extraction and feature classification. Pre processing includes the removal of artifacts in the EEG signals due to some noises like eye blinking, etc. Discrete Wavelet Transform is used for the feature extraction. Features in both time domain and frequency domain are evaluated on the various EEG Signals. Based on the features extracted from various EEG signals, they are classified for the different finger movements using SVM classifier. The accuracy of 96.67% has been achieved for the proposed finger movement’s classification.
IN-VITRO STUDY ON Α-AMYLASE INHIBITORY ACTIVITY AND PHYTOCHEMICAL SCREENING OF MINT LEAVES AND ANNONA SQUAMOSA LEAVES HAVING ANTI-DIABETIC PROPERTIES

α-amylase inhibitors are used in the treatment of type II diabetes mellitus. This study aims to identify the α-amylase inhibitors from two medicinal plants Mentha piperita and Annona squamosa. Primarily, the extract of both leaves was prepared using methanol and water and perform the screening of phytochemicals constituents present in the extract. Aqueous and methanol extract of mentha leaves contains like glycosides, steroids, alkaloids, flavonoids, tannins, saponins and A. squamosa leaves extract contains flavonoids, triterpenoids, tannins, and quinines. Furthermore, the α-amylase inhibitors of both plant’s aqueous and methanol extract was analyzed by spectroscopic method. The results indicated both plants whose methanolic extracts exhibited highest α-amylase inhibitory activity were M. piperita and A. squamosa with IC50 of 30.84±1.03 and 47.78±2.45 μg/ml, respectively. This finding could be used to support the use of these plants for treatment of diabetes and also it is an interesting phyto-chemicals to be developed as a new drug.
Survey on Sentiment Analysis and Sentiment Classification

Opinions are the fundamental aspect to almost all decision making activities. The increased usage of internet and the exchange of user opinions through social media and public forums on the web has become the motivation for sentiment analysis. Due to the infinite amount of user opinions available throughout the web it is necessary to automatically analyze and classify sentiment expressed in opinions to make the decision making process an easy task. Opinion Mining or Sentiment Analysis is a Natural Language Processing technique that attempts the system to automatically identify and extract sentiments expressed in user reviews. The basic task of sentiment analysis is sentiment classification which classifies a user review as positive, negative, neutral. This survey gives an overview of sentiment analysis, sentiment classification, methods used for sentiment classification.
EXTRACTION, CHARACTERIZATION AND APPLICATIONS OF CHITOSAN FROM FISH SCALES

Chitosan was extracted from fish scales by a sequence of chemical processes involving Demineralization, Deproteinization and Deacetylation. The chitosan extracted had been characterized using FTIR analysis. The absorption band reveals the characteristic feature of chitosan polysaccharide and the degree of deacetylation. The anti-oxidant activity was studied by DPPH scavenging assay. The Anti-microbial activity was carried out by agar well diffusion method for the organisms viz. Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Aspergillus niger and Aspergillus terreus. The extracted chitosan had been analysed for its effects on seed germination. The chitosan extracted showed good anti-oxidant activity and also promoted seed germination. KeywordsChitosan, Demineralization, Deproteinization, Deacetylation, FTIR, Anti-microbial, Anti-oxidant, Seed germination.
Classification of Breast Cancer Using SVM Classifier Technique

: This paper proposes a technique for classifying the breast cancer from mammogram. The proposed system aims at developing the visualization tool for detecting the breast cancer and minimizing the scheme of detection. The detection method is organized as follows: (a) Image Enhancement (b) Segmentation (c) Feature extraction (d) Classification using SVM classifier Technique. Image enhancement step concentrates on converting an image to more and better understandable level thereby applying Median filtering approach for reducing the noise on an image. Then the Contrast stretching is applied to increase the contrast of the image. The main part of cancer detection is segmenting the breast image to improve the diagnosis and detection of breast cancer. The segmentation used here is Thresholding. Next the features are extracted from the cancer segmented area and classified the cancer according to its feature by using Support Vector Machine (SVM) classification technique. The method was tested for 119 mammogram image from the mini-mias database
Investigate the Effect of Knowledge Management on Customer Relationship Management (Case Study: Mellat Bank, Yazd City)

Nowadays creating and maintaining customer relationship is discussed not only to sell products and services, but also to access information and knowledge a new concept in the form of knowledge management systems. The main purpose of this study was to investigate the relationship between knowledge management and customer relationship management in the branches of Mellat bank in Yazd city. This study is applied research in term of purpose and descriptive and field to collect data collection. Statistical society of this study includes all employees, assistants and administrators working in Mellat Bank of Yazd city. 170 samples were selected using Cochran Formula. Then, comprehensive information on the relationship between knowledge management and management of customer relationships acquired by distributing researchers made questionnaires. In this study, descriptive analysis was used to display demographic characteristics of statistics as well as deductive statistical tests including Pearson correlation analysis and structural equation modeling (SEM) was used to investigate and prove the hypotheses.
CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE

The performance of Content-Based Image Retrieval (CBIR) system is depends on efficient feature extraction and accurate retrieval of similar images. Content based image retrieval is the task of retrieving the images from the large collection of database on the basis of their own visual content. This paper combines color features using Color Descriptor (CN) and texture features using Gray Level Co-occurrence Matrix (GLCM) to obtain better retrieval efficiency.From large database,using these feature vectors near about similarly matched images are retrieved. It is observed that combination of color features with the texture provides approximately similar results with very less processing time if we compare with individual approach.
ROUTING PROTOCOL FOR UNDERWATER SENSOR NETWORK WITH EFFECTIVE LOAD BALANCING USING NETWORK MANAGEMENT SYSTEM

The underwater sensor network (UWSN) is one of the most predominant research areas for gathering information from unexplored and inaccessible regions under the sea water. There are various areas exist which require the UWSN such as ocean bottom data collection, offshore discovery, disaster avoidance, pollution monitoring, navigation and surveillance. Due to these applications, the various researchers are interested to use this technology in the subsea applications. The underwater environment is completely different from ground based network such as high dense salty water, electromagnetic and optical signals cannot be transmitted for long distances in ocean because of scattering, high attenuation and absorption effect. Due to these challenges and high movement of nodes by water flow, end-to-end routing methods used in existing routing protocols are not applicable to underwater environments. Therefore, we have required to a new routing protocols for underwater sensor networks (UWASNs). In this paper, we have introduced a Network Management system (NMS) which will manage whole network. Load balancing is also one of the important issues for balancing the load on network, and choosing the optimal route for transferring the data. Our aim is to design a routing protocol for proposed system architecture through the Network management system and make system more reliable, energy saving, scalable and fit for long term applications.
Feature Representation Scheme for Smart Video Sensor

In Jakarta and other cities, the police and the Department of Transportation are still monitoring traffic videos manually. They have difficulties when some of the traffic problems occur simultaneously. Therefore, they need a Smart Video Sensor that can automatically provide the latest information about traffic conditions. The information generated Smart Video Sensor are features . Data inputs from Smart Video Sensor are scenes . The working principle of this video is a streaming video that records scene frame by frame. Then, the Smart Video Sensor will extract the features of the frames. After that, the Smart Video Sensor will provide a feature representation scheme in the form of XML. Furthermore, the Smart Video Sensor will send features in the form of XML representations to TMC. This paper contributes to representing the features of the objects of traffic in XML (eXtensible Markup Language). XML format is selected to represent the features because of its dynamic XML structure. The test results showed that the representation of the features can be read and viewed accordingly.
Research of an Iron Oxide Nanoparticles and Potential Application

The iron oxide nanoparticles (FeNPs) are widely used in biomedicine for good biocompatibility. To promote its safe application, any potential nanotoxicity should be thoroughly and carefully investigated. This paper systematically summarizes our lab’s research on the nanotoxicity of iron oxide nanoparticles coated with dimercaptosuccinic acid (DMSA), including the effects of FeNPs on viability, apoptosis, cycle, and oxidative stress at cell level. In vitro studies revealed that the FeNPs showed obvious apoptosis of human acute monocyte cells (THP-1) and human hepatoma cells (HepG2) at the highest concentration. FeNPs resulted in common and cell typespecific nanotoxicities of the FeNPs to both human and mouse cells at the gene, disturbed cell’s iron and osmosis homeostasis by the internalization of FeNPs through releasing iron ion in cells, resulted in cytotoxicity of DMSA as coating molecules of FeNPs and the inhibitor of DNA binding/differentiation (Id) related nanotoxicity of FeNPs at gene level. The studies of our lab shed many new insights into the nanotoxicity of the nanoparticle. Furthermore, the toxicity may play more value if it is guided and applied reasonably, such as iron supplement, anti-oxidation, and immunotherapy.
Updates in laparoscopic surgery for perforated peptic ulcer disease: state of the art and future perspectives

Peptic ulcer disease (PUD), which affects 4 million people worldwide annually, has a perforation rate of between 2% to 14%. Perforated peptic ulcer (PPU) carries a substantial morbidity and mortality. Surgical repair remains the treatment of choice for a PPU. This literature review includes the latest updates in laparoscopic repair techniques for a PPU and also focuses on its causation, clinical features, diagnostic workup, laparoscopic and open surgical techniques, outcomes, and future perspectives. An extensive review of the literature was performed, and the most recent meta-analyses and reviews concerning the topic were considered. No substantial differences were found in postoperative mortality between the open and laparoscopic approaches for patients with a PPU. Furthermore, a laparoscopic repair lead to statistically significantly less postoperative pain and carried a lower risk of wound infection. Hence, a laparoscopic repair is justified, where situationally appropriate, as the treatment of choice.
Query Expansion Algorithm with Metadata Support for Ontology Matching

—To improve the precision and recall of current IR systems, search engines that work on meaning of data should be implemented. Semantic Web provides meaning to the data. Data is written in an ontology format. The core concept of semantic search engine is ontology mapping. Every information, documents, books etc available in the World Wide Web will be replaced by ontologies in the near future. So, for an efficient information retrieval, user provided search query must be expanded to perform matching. In this project, an algorithm to perform ontology matching is proposed.
Swahili Text Classification using Support Vector Machine and Feature Selection to Enhance Opinion Analysis in Kenyan Universities

: In Kenya’s social media, Nairobi Swahili is the norm for communication in institutions of higher learning. Extant studies dwells on standard Swahili, affording limited text classification literature for Nairobi Swahili Natural Language Processing. The research explores how social media experience provides new ways for interactions, resulting into new challenges in managing student concerns that now require new knowledge for decision-making. Students have taken advantage of social media platforms by creating virtual discussion forums, which are quickly becoming repositories of collective knowledge. Unfortunately, institutions of higher are not able to utilize the collected knowledge through these platforms. The focus of this research is to ensure knowledge generated via social media is useful through opinion mining to enable extraction, classification and storage to support decision-making. Different algorithms were tested utilizing data from popular social media; operated by students in Kenyan universities. The results showed that SVM gives the best results when used with Linear Kernels and better performance on TF-IDF with N-grams methods. An analysis on the different SVM kernel showed linear kernel to have a better performance at 80% compared to Polynomial kernel and Radial Basis Function kernels, which both stand at 57%. To choose the best feature selection method for use along with linear SVM, TF and TF-IDF were tested. TF-IDF performed better with N-grams at 83%; rendering this research both theoretical and practical significance. The research would provide fast hand information for decision support in Kenyan higher learning institutions using text-mining tools in social media.
EMULGEL: A NOVEL TREND IN TOPICAL DRUG DELIVERY SYSTEM

For the treatment of local as well as systemic skin disorders, topical drug delivery systems have been used for centuries offering the advantage of delivering the drug directly to the site of action and also delivering the drug for extended period of time at the affected area. When gel and emulsion are used in combined form the dosage form are referred as Emulgel. Since they contain dual control release systems: a gel and an emulsion, emulgels have emerged as one of the most interesting topical delivery system. They are emulsions of either oil-in-water or water in oil type, which are gelled by mixing with a gelling agent. The major objective behind emulgel is delivery of hydrophobic drugs via skin. Thus emulgels have major advantages on novel vesicular system as well as on conventional systems in various aspects. They are referred for topical use due to their favorable properties such as being greaseless, non-staining, thixotropic, emollient, easily removable, water soluble, bio-friendly, long shelf-life, transparent and pleasant appearance. The use of emulgels can be expanded in different classification namely analgesics, anti-inflammatory, anti-fungal, antiacne drugs and various cosmetic formulations.
Solid Dispersions : An Approach to Enhance the Bioa vailability of Poorly Water-Soluble Drugs

Improving oral bioavailability of drugs those give n as solid dosage forms remains a challenge for the formulation scientists due to solubility problems. The dissolut i n rate could be the rate-limiting process in the absorption of a drug from a solid dosage form of relatively insoluble drugs. Therefor increase in dissolution of poorly soluble drugs b y solid dispersion technique presents a challenge to the formulation scientists. Solid dispersion techniques have attracted conside rabl interest of improving the dissolution rate of highly lipophilic drugs thereby improving their bioavailability by reducing drug p article size, improving wettability and forming amorphous particles. The te rm solid dispersion refers to a group of solid prod ucts consisting of at least two different components, generally a hydrophilic inert carrier or matrix and a hydrophobic drug. This art icle reviews historical background of solid dispersion technology, limitati ons, classification, and various preparation techni ques with its advantages and disadvantages. This review also discusses the recen t advances in the field of solid dispersion technol ogy. Based on the existing results and authors’ reflection, this review give r ise to reasoning and suggested choices of carrier o r matrix and solid dispersion procedure.
DISTRIBUTED RESTRAINING MALWARE PROPAGATION IN MOBILE SINK ROUTING FOR WIRELESS SENSOR NETWORKS

Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Variety of sensing capabilities results in profusion of application areas. However, the characteristics of wireless sensor networks require more effective methods for data forwarding and processing. In WSN, the sensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication under these conditions. In this paper, we give a survey of routing protocols for Wireless Sensor Network and compare their strengths and limitations.