Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Major Animal Models Relevant to Contemporary Orthopaedic Repair of the Appendicular Skeleton in Humans (Part 1: healing in the presence of bone defects, non-union and inter-current disease)

Animal models have long been used as a bridge from in vitro to the human patient to provide insights both as a precursor and valued addition to human-based research. Many animal models have been developed to investigate bone regeneration and repair. Animal models continue to have a place in orthopaedic innovation and development. Ethical constraints as well as reasons of experimental design limit direct progression to human clinical studies. Used with discretion to limit the impact on animal welfare, each species provides a unique perspective on the performance of prostheses, healing and repair processes in different scenarios. Although results often cannot be directly ‘translated’ to human patients, these provide invaluable windows of enlightenment to broaden our perspective on future innovative approaches. Some species such as the fish are cheap and bone changes can be directly visualised in living subjects. Others such as the mouse have been selectively bred for genotypic traits. Larger animal models may be more suitable in terms of size, to test scale versions of prosthetics and to evaluate mechanical properties. The objective of this paper is to review animal models relevant to contemporary orthopaedic surgery of the appendicular skeleton. To help guide readers to relevant data, we have structured this review by major animal species and separated the models into those considering either mechanical healing (including bone defects) and healing in the presence of other disease factors including infection to provide a resource to help future researchers locate definitive study references. Metabolic disease including osteoporosis and comparative imaging are considered separately in the follow-on review paper. The intention is to provide a resource for quick reference of established models in major non-primate species and to maximise the value of previous work on animals models used to study orthopaedic repair in the appendicular long bones. ;
Real Time Position Control of Autonomous Robot using Still Camera Images

— Now a days , most of the robot are operates and control by using wired or wireless. This paper will describe the concept of wireless communication system on which the robot is operated using personal computer (PC). Graphical user interface is used for robotic operation on personal computer using MATLAB. This paper gives the idea of real time position control of autonomous robot using still camera. Real time source to destination images coordinates are given to the robot and robot will move further to the given path. This system will be useful for drone attack in military robots
Indispensable for Preventıve Dentistry: Fissure Sealants

In recent years, the greatest interest of modern dentistry; it focuses on reducing the risk of caries, preventive practices, and non-invasive conservative techniques where dental structures are protected as much as possible [1]. In previous caries research, occlusal surfaces have been found to susceptible to caries due to pits and fissures resulting from anatomical structures. Especially in children and young people, caries are most commonly seen in fissures [2]. The most effective method to prevent caries from these areas is the application of pit and fissure sealants. Pit and fissure sealants, one of the basic methods of preventive dentistry, are increasingly used in developed countries with the purpose of prevent occusal surface caries [3]. Fissure sealants are prevent plaque microflora and food-borne debris from accumulation in caries-susceptible pits and fissures. In addition, it reduces the effect of acid formed by the bacteria causing caries and remineralises the new lesions of the caries [4].
on Communication, Computing and Information Technology

Popular payment mode accepted both offline and online is credit card that provides cashless transaction. It is easy, convenient and trendy to make payments and other transactions. Demonetization process operated by India&apos;s Prime Minister Narendra Modi seems to be taken major changes in cashless economy. Credit card fraud is also growing along with the development in technology. It can also be said that economic fraud is drastically increasing in the global communication improvement. It is being recorded every year that the loss due to these fraudulent acts is billions of dollars. These activities are carried out so elegantly so that it is similar to genuine transactions. Hence simple pattern related techniques and other less
A Result Evolution of An Artificial Immune System for Intrusion Detection System to Improve the Detection Rate

: This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system. The proposed intrusion detection system will use the concepts of the artificial immune systems (AIS) which is a promising biologically inspired computing model. AIS concepts that can be applied to improve the effectiveness of IDS.
Pharmacognostical, Antioxidant and Antimicrobial studies of aerial part of Pulicaria crispa (Family: Asteraceae)

The study was designed to investigate the active phytochemicals present in the methanolic extract (ME) of Pulicaria crispa (P. crispa). The antimicrobial and antioxidant activities were also explored. These investigations were carried with an aim to use this plant in future. The morphological, microscopical and physicochemical investigation was carried out before the investigation of phytochemicals for authentication purpose. The antimicrobial activity was tested against Gram-positive, Gram-negative bacteria, Aspergillus niger and Candida albicans. Ampicillin was used as a standard for comparing the zone of inhibition. The in vitro 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and Ferric reducing ability (FRAP) models were used for the investigation of antioxidant activity. The results of the authenticated plant, phytochemical examination was shown the presence of biological active compounds. Marked antimicrobial and antioxidant activities were observed which may be due to the presence of tannins, phenols and flavonoids in the ME. The outcome of the present finding was suggested that it may be a good source of active antibacterial and antioxidant phytochemicals.
Effect of Flaxseed Flour on Physio-chemical and Sensory Acceptability of Chicken Nuggets

The present study was taken up to enrich the fiber and healthy fatty acid content in chicken nuggets with addition of flaxseed flour. The chicken emulsion is prepared with fortification of flaxseed at different level viz., 1.0, 2.5, 5.0 and 10.0 percent respectively. The products were subjected for proximate analyses, physio-chemical and sensory analysis. The samples were analyzed as raw emulsion batter and cooked chicken nuggets. On analysis of proximate composition of nuggets the moisture retention, fiber, ash and fat content has significantly (p<0.05) increased. The pH and TBARS values represent stability and found to be low in flaxseed fortified chicken nuggets. The sensory acceptability with 2.5 percent addition of flaxseed had higher acceptance compared to others. The present study concludes that 2.5 percent addition of flaxseed has increased the nutritive value of chicken nuggets without much affecting the sensory acceptability of the product and increased level leads to sensory rejection.
An Endowed Takagi-Sugeno-type Fuzzy Model for Classification Problems

— Takagi-Sugeno-type fuzzy classification model is improved by proposing a new incremental learning approach. Margin selective gradient descent learning and incremental support vector machine helps the proposed fuzzy model to learn with high generalization ability. Training samples are provided incrementally one after another instead of in a single batch. This improved fuzzy model is obtained from an empty set and clustering algorithm is used for finding initial fuzzy sets and number of rules in the antecedent part. The generalization ability of the fuzzy model can be improved by proposing an online incremental linear support vector machine to tune the rule consequent parameters. Online training problem can be solved by incremental support vector machine, where only one new training sample is provided at a time. Margin selective gradient descent algorithm is used to learn antecedent parameter and avoids overtraining.
Multi Biometric Cryptosystem Based on Decision Level Fusion

Biometric cryptosystem that provide an innovative solution for encryption, cryptographic key generation and biometric template protection. Security and authentication are the two major factors in biometric cryptosystem. In biometric cryptosystem original templates are replaced by a helper data, which assist in recovering the cryptographic keys. There are two major criteria for judging the performance the biometric cryptosystem, they are accuracy and security. The accuracy of biometric cryptosystem is similar to that of biometric authentication system and the security of biometric cryptosystem that requires helper data, that is once compromised by an attacker that should not reveal significant information about the original template. The proposed method can be classified in to two types: feature transformation and biometric cryptosystems. Here we construct a Multibiometric Cryptosystem (MBC) using decision level fusion. Here multiple fingers are used for protecting the data. Accuracy and security of single biometric cryptosystem (SBC) are limited compare to multibiometric cryptosystem. MBC offer higher authentication accuracy and flexibility.
A NEW ALGORITHM INTRODUCED TO OPTIMIZE THE LIFESPAN OF WIRELESS SENSOR NETWORK

In this paper, a different scheduling method has been introduced through the construction of a new algorithm, with the purpose to elongate the lifespan of wireless sensor network. At all times, half of the full coverage of sensors is provided. As a result, the information on the distances between the sensor’s neighbor nodes and its sensing areas are the only requirement for each sensor in the new algorithm. Furthermore, based on the simulations, noticeable improvement can be seen from the lifespan of several existing lifespan maximization algorithms, which is a result of the newly proposed algorithm. Besides, the runtime of sensor network is improved with this new algorithm. Following that, a comparison with other algorithms has been done in order to show the accuracy of new algorithm.
E VALUATION OF T EXTURAL F EATURE E XTRACTION FROM GRLM FOR P ROSTATE C ANCER TRUS M EDICAL I MAGES

Ultrasound imaging is one of the promising techniques for early detection of prostate cancer. There are five steps involved in processing the ultrasound images such as pre-processing, segmentation, feature extraction, feature selection, and classification. In this paper, the Transrectal Ultrasound (TRUS) images are pre-processed with M3 filter. Then it is segmented by using DBSCAN clustering after applying morphological operators, in order to extort the prostate region. It is proposed to extract the features by using Gray Level Run Length Matrix (GLRLM) for different direction from the segmented region. To classify the images into benign or malignant, Support Vector Machine (SVM) is adapted to evaluate the performance of the proposed method through classification. Over 5500 digitized TRUS images of prostate are used for the experimental analysis. The results obtained using the classification showed that texture features based on GLRLM by using combined directions ( θ = {0º, 45º, 90º, 135º}) distinguish between malignant and benign on TRUS images, with highest accuracy 85% where as sensitivity and specificity of 82% and 100% respectively.
An Accurate Allocation of Reliability and Cost Estimation for Architecture-based Software using DRMPA

— All traditional system reliability allocation is conducted on the specified reliability requirement alone. The all approaches are not used the reliability requirement together with a CL. In this paper, we will propose a new approach for allocating the reliability and setting the initial level reliabilities to all components of the system with confidence level approach and we proposed Dynamic Risk Management Programming Algorithm(DRMPA) for Reducing the risk cost from the expected cost and finally we compared the Dynamic Programming Algorithm(DA),At the end we proposed the best reliability allocation with maximum and required reliability of the system with minimum development cost is using proposed approach.
PREDICTIVE ANALYTICS OF HEALTHCARE DATA

Predictive analytics is employed to improve the ability to take precautionary measures during medical emergencies. In health care, the sensor-based data are generated daily which can be used to predict future data using regression model. In this paper, pain dataset from integrating data for analysis, anonimyzation, and sharing repository is used for experimenting different machine algorithms. The results show that logistic regression gives more accuracy than other algorithms.
Co-extracting Opinion Targets and Opinion Words from Reviews

: Extracting opinion targets and opinion words from online reviews are two fundamental tasks in opinion mining. This paper proposes a novel approach to collectively extract them with graph co-ranking. First, compared to previous methods which solely employed opinion relations among words, our method constructs a heterogeneous graph to model two types of relations, including semantic relations and opinion relations. Next, a co-ranking algorithm is proposed to estimate the confidence of each candidate, and the candidates with higher confidence will be extracted as opinion targets/words. So this project presents an innovative model for relevance topic review discovery to solve the problems.
Multi-Parametric Health Monitoring System

This article proposes the concept of Disease Detection Algorithm (DDA) for health monitoring. Health is a dynamic process; it is the level of metabolic efficiency of a living organism. The general abnormal condition of a person’s body depends on several vital parameters related to health and sometimes results in chronic diseases. These diseases include minimum parameters like heart rate, temperature, blood-pressure, SpO2, weight etc. To this aim, a system is proposed enabling the monitoring of vital signs and sending data to the physician to monitor patient and have doctor’s intervention in critical situations. The disease detection algorithm (DDA) promises to provide higher accuracy of disease detection. Chronic Heart Failure, Hyperthermia, Obesity /Type 2 Diabetes, Type 1 Diabetes, Dysautonomia etc are commonly detected diseases in the proposed algorithm. Wireless Sensor network allows early home interventions thus reducing the number of subsequent hospitalizations system. Keywords—Wireless Sensor Networks, health at home monitoring, DDA algorithm, vital sign sensors, e-health
PUTATIVE VIRULENCE FACTOR AND ANTIMICROBIAL SUSCEPTIBILITY OF LOCALLY BACTERIA AEROMONAS HYDROPHILA ISOLATED FROM SURFACE TIGRES RIVER IN BAGHDAD, IRAQ

The genus Aeromonas (family Aeromonadaceae) are medically important, Gram-negative, rod-shaped and are ubiquitous in aquatic environments.25 environmental isolates positive tested for,biochemical including, oxidase, negative string test,growth in 0 and 1 % NaCl, , growth in 7 % NaCl. All isolates of environmental A. hydrophila were a positive result Lecithinase , DNase, protease produces and hemolysin . The results showed that all isolates were sensitive to chloramphenicol, tetracycline. Resistance to streptomycin, cephalothin erythromycin and nalidixic acid,92%,80%,36% and 20% respectively.A. hydrophila caused, villi atrophy and fibrosis with inflammatory cells reached to mucosa , in liver , the effect hepatocytes in apoptosis with pyknotic nuclei and wide spread necrosis of hepatocytes with mononuclear leucocytes infiltration of the area.
Charactersation of Oral Cancer Lesions Using Texture Features

— Oral cancer is a serious problem among people due to its aggressive nature, associated with mostly unfavorable prognosis. Clinical examination by experienced medical experts followed by biopsy for diagnosis are time taking. Identification in early stage always helps for better curative measures. An effective image processing techniques were used with watershed segmentation including GLCM texture features extraction, from the analysis of true color images in MATLAB software computed data and analyze images with some useful algorithms. Image processing techniques with marker controlled segmentation and feature extraction enables clear visualization of cancer effected areas with substantial resolution detecting different types of oral cancer lesions. The techniques are rapid, accurate and different than clinical with laboratory investigation for diagnosis for early effective curative measures.
Study on prevalence of pregnancy complication and patient's awareness at Dhaka city in Bangladesh

Complications during pregnancy are very much common in our country, but majority of the women do not have enough knowledge about this complications. So this current study was aimed to find out the prevalence of pregnancy related complications and patients’ awareness about these complications at Dhaka city in Bangladesh. This was a survey based study where pregnant women from different hospital at Dhaka city were interviewed with a questionnaire. All the women in our study were in their third trimester of pregnancy. We have found 230 pregnant women with various complications among 300 sample population. Majority of the patients had hypertensive disorder (35%), anemia (26.33%). Some patients had UTI (4%), hepatitis (4%). We have found very insignificant amount of patients with diabetes and thyroid disorder. Among 230 patients with complications 18.66% had problems before their pregnancy and 76.66% had problem in their current pregnancy. The awareness level about these complications among the respondents was not satisfactory. In case of hypertensive disorder, the most practicing awareness factors were drinking sufficient water in a day, taking enough rest, taking prescribed medications. Majority of the anemic patients were aware about taking folic acid or vitamin B12, taking vitamin C containing foods or fruits and also taking the prescribed medication. Almost all the respondents learnt about these complications from their doctors. We have seen that many of them were not concern about taking proper antenatal or postnatal care. So there should be adequate steps in our country to make the women conscious about the total pregnancy period and also make the arrangements for practicing all the steps easily that will help the women to give birth their baby safely.
Telugu to English Translation using Direct Machine Translation Approach

- The motivation behind working on a translation system from Telugu to English were based on the principles that a) There are many translation systems for translating from English to Indian languages but very few for vice versa. Telugu is a language that exhibits very strong phrasal, word and sentence structures next to Sanskrit, which makes the work organized on one hand but complex in handling on the other. This work demonstrates one such machine translation (MT) system for translating simple and moderately complex sentences from Telugu to English.
The Research Application Situation And Outlook Of Moving Target Detection And Tracking Technology

: With the rapid development of information technology and the increasing importance of security te chnology, the design and realization of the intelligent monitoring system has become particularly important. Tar get tracking is a key part of video surveillance system, which is the foundation of pattern recognition, behavior a nalysis and other advanced signal processing. Therefore, the target detection and tracking is an important researc h project. At present, aimed at one or more target tracking has already had many kinds of algorithm methods. T his article mainly briefly outline the characters and development situations of different methods and give a brief introduction to the situation of application of target tracking technology. For related personnel reference.
A perusal on Facial Emotion Recognition System (FERS)

Emotion could be well interpreted using the facial expressions. Several approaches have been proposed for classification of emotion (human affective states). We in this paper steadily examine available approaches for solving the problem of machine understanding of facial emotion and compare important issues like the collection and availability of training and test data. We finally outline the challenges we would work on to advance facial emotion recognition based on the explored latest work of various authors. We conclude this paper with the improvements and further advancement needed in facial expression recognition
Despeckling of Ultrasound Images Using Modified Local Statistics Mean Variance Filter

This article presents an improved method of despeckling the ultrasound medical images. In this paper a modified local statistics mean variance filter method has been proposed. In the proposed method, more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error, peak signal to noise ratio, quality index, and structural similarity index measure are calculated to analyze the quality of the despeckled image.
An Automated Brain MRI image segmentation using Generic Algorithm and TLBO

In this paper, automated brain MRI image segmentation based on Teaching-learning based optimization. The proposed method is the combination of TLBO and a generic algorithm based segmentation method, which is named variable string length genetic segmentationstrategy (VGAPS) which is capable of evolving the number of segments present in the image automatically. The allotment of pixels to the segment is based on Line symmetry based methods which is much efficient and effective than Line symmetry and Euclidean distance based methods. The cluster enters were deployed inside the chromosome whose value changes in each iteration based on the fitness calculated through the TLBO. The result demonstrates that the proposed technique is well suited for medical image segmentation with satisfactory result.