Miracle-dz/UniFAR-FTUs huggingface dataset:
K-Nearest Neighbor Generation in External Memory Model

: Clustering is the process of grouping datasets into subsets of data based on some similarity criteria. The process of clustering comes in the category of unsupervised learning. In unsupervised learning method no information about object for right answer is provided. Main objective of clustering is to divide groups having similar features and assign them as clusters. Most of the algorithms of clustering considers that the main memory of a computer system is of finite size and can contain some sets of patterns which are not able to fit in main memory. Most of the part of data set is to be stored in the portion of secondary memory when the size of data set is quite vast. The disk input output operations act as major barrier in developing efficient algorithms of clustering specially for large data sets. Wide variety of different techniques with different designs are used to design clustering algorithms for large data sets. In this paper, the algorithm is designed to maintain the performance in case of large data sets where some values or locations in memory for corresponding values are intermittently accessed. External memory algorithms are suitable to use in large data sets. The algorithm shown in this paper is designed in external memory model and also the input output operational complexity is reduced.
Meaning representation Meaning representation Meaning representation Meaning representation for automatic indexing for automatic indexing for automatic indexing for automatic indexing of Arabic texts of Arabic texts of Arabic texts of Arabic texts

The aim of indexing is to identify the words that represent the main idea of a paragraph or a specific text, in the framework of the representation of the meaning in an automatic treatment (NLP) of the Arabic; we propose a model based on conceptual vectors. These vectors try to represent the whole of ideas contained in textual segment (word, expression, texts …) . This model lean on modern linguistic conception the semantic field theory. By basing itself on the semantic relations (synonymy, homonymy.) between the words, we use these fields for the construction of semantic field data base and of a vectorial space then we calculate the meaning of textual segments in the semantic fields. Finally we use this model for indexing the text.
TITLE: VIDEO INPAINTING WITH IMAGE INPAINTING USING SUPER RESOLUTION METHOD

This Image is playing an important role in our life. An Examplar-based inpainting method is introduced in this paper. Inpainting low of resolution images are easier than that of high resolution images which reduce the computation complexity and less sensitive to noise. With different configurations, the low resolution input image is inpainted several times for the purpose of parameter setting & results are finally combined and the super resolution method increases the quality of image or recovers the actual image. Using different inpainting techniques low resolution image is in painted & high resolution in painted image are formed by combining result using single image super resolution algorithm
Enhanced Key Aggregate Searchable Encryption For Group Data Sharing Via Cloud Data Storage

: In this paper we Taking into consideration of the realistic problem of privacy-preserving data sharing system based on public cloud storage which is need a data owner to allocate a large number of keys to users to permit them to access the documents, In this proposed concept of key aggregate searchable encryption (KASE) and construct a concrete KASE scheme. It can provide an efficient solution to building practical data sharing system based on public cloud storage. In a KASE scheme, the owner needs to distribute a single key to a user when contributing a more no of documents with the user and the user needs to submit a single trapdoor when they query over all documents shared by the same owner. On the other hand, if a user wants to question over documents shared by multiple owners, that user must produce multiple trapdoors to the cloud. as a proposed work we suggested STMO-KASE(SingleTrapdormany owners-KASE)schemes which leads to decrease the number of trapdoors under manyowners setting by attaining the security.
Robotic assisted single site surgery: a decade of innovation

The dawn of the era of single incision minimally invasive surgery, or single port surgery, was first realized with laparoscopy. Beginning with simple procedures surgeons began to apply this concept to a growing variety of procedures. Despite demonstrating its feasibility and associated favorable patient outcomes, single port laparoscopy remained a challenging procedure and was unsuccessful in gaining significant traction. With the addition of the robotic surgery, single port surgery was dramatically changed. With the aid of the robotic platform, surgeons were able to overcome the challenges and limitations of laparoscopy. Since the first reports of robotic assisted single port surgery in 2009, surgeons across all specialties and subspecialties have found application in their practice. In this review, we highlight the last decade of single site robotic surgery and discuss the current challenges.
Blocking Implication Attacks on Social Network Private Information

: In the cloud computing environment load balancing has a major effect on the presentation. Good load balancing creates cloud computing most effective and progresses client fulfillment. This thesis initiates a good load balance design for the public cloud depending upon the cloud separation thesis with a switch mechanism to select various approaches for various circumstances. The algorithm implies the game hypothesis to the load balancing approach to get better the effective in the public cloud atmosphere
Impact of virtual reality games as an adjunct treatment tool on upper extremity function of spastic hemiplegic children

Aim: This study was conducted to investigate effect ofvirtual reality games (VRG) as an adjunct treatment tool on upper extremity function in management of spastic hemiplegic children. Methods and subjects: the study was conducted on forty spastic hemiplegic cerebral palsied children; ranged in age from 5 to 10 years old. They were divided into two equal groups; the control group that received selected physical therapy program and the study group that received the same program in addition to VRG. Both groups were evaluated with Peabody Developmental motor scale PDMS-2 and Abilhand Kids questionnaire. Results Object manipulation and visual-motor skills of PDMS-2 and upper limb functions were measured before and four months post treatment. Significant results in both groups were noted in all measuring variables. Object manipulation, visual-motor skills and upper limb functions were significantly improved in study group post treatment compared to control one. Discussion and Conclusion significant improvement of object manipulation, visual motor skills and upper limb functions in study group post treatment are related to active participation of children in simulating environment, driven their active motivation and enhance their participation through self-competition activities. VRG can enhance active participation of children with motor deficits in majority of upper limb activities through consideration of child personality and changing of environmental factors.
Security System Design Based on Human Face Detection and Recognition on Android Platform

: There are various electronic security systems available, based on human motion sensing, biometric features sensing, code or password identifications etc. In this system human face detection and recognition is used for opening and closing the doors, by means of switching ON and OFF of relays. By relays the various motors can be operated to door movement. The system is developed on android platform i.e. an android OS is installed on hardware and an android app is running on android OS. The hardware platform is ARM11 based development board. In this system the human face is detected and then recognized with faces present in the database of system. For face detection „Haar feature-based cascade classifiers‟ algorithm is used. And for face recognition “Principal Component Analysis” (PCA) algorithm is used. For both algorithms OpenCV and JavaCV libraries are used. Application is developed in JAVA in eclipse IDE . For actuation relays the Atmega AVR microcontroller is used. The ARM11 board gives the command to the AVR microcontroller via RS232 comm. Protocol.
Detecting Leakage of Sensitive Data Using Water Marking Technique

- In recent years, Data leakage is the big challenge in front of the research institutions and government organizations. Though there are number of systems designed for the data security by using different encryption algorithms, there is a big issue of the integrity of the users of those systems. It is very hard for any system administrator to trace out the data leaker and data leakage among the system users and data. In this paper, we propose a privacy preserving data-leak detection solution and watermarking techniques which can be outsourced and be deployed in a semi honest detection environment. Using watermarking model, we can provide security to our data during its distribution or transmission and even we can detect if that gets leaked.
A LIGHT WEIGHT LOCATION VERIFICATION PROTOCOL BASED ON BEHAVIOR LEARNING PROCESS FOR MOBILE ADHOC NETWORKS

— Fast changing and growing nature of mobile adhoc networks makes the accessibility of location aware services becoming difficult. With the presence of adversaries and attacking nodes, the location based service discovery becomes a challenging one. We propose a light weight location verification protocol for the verification of nodes which turns out to be a robust one and uses the behavior of nodes for the verification process. Here distributed one step Location Verification Protocol is being used and session based behavior learning process for the identification of adversary nodes has been adopted. The proposed method is indeed capable of preventing attack types such as node impersonation , Sybil, ddos attacks.
Physical, Physico-Chemical and Chemical Properties of Two Maize Varieties (BR-9928-DMR-SY and TZL-Comp-4C2)

Physical, physico-chemical and chemical properties of two maize varieties (BR-9928-DMR-SY and TZL-Comp-4C2) were evaluated. The physical properties [length, diameter, thickness, thousand seed weight (TSW), thousand seed volume (TSV) and bulk density of the maize grains were evaluated. The two maize varieties BR9928-DMRSY and TZL comp 4C2 were sorted by hand to remove stones, chaff, and damaged grains and milled into flour and their colour parameters, physico-chemical [gelation, swelling, Water Absorption/Oil Absorption Capacities (WAC/OAC), bulk density, amylose content and pasting characteristics] and chemical (fat, ash, protein, moisture, starch, sugar and mineral contents) properties were determined using standard methods. Data were statistically analysed at P=0.05. Colour parameters showed significant difference (p<0.05), TZL Comp4C2 had 84.60%, 0.35% and 22.20% while BR9928 DMR-SY had 79.17%, 5.45% and 41.10% for lightness (L*), redness (a*) and yellowness (b*) respectively. No significant difference was observed for the physical parameters tested. TZL Comp 4C2 showed a higher pasting temperature and trough than BR9928 DMR-SY. While, BR 9928-DMR-SY showed a higher breakdown than TZL Comp 4C2. Higher amylose content (27.18%) was obtained for the TZL Comp4C2. Fat, protein, ash, starch and mineral content showed higher values for BR 9928 DMR-SY than TZL Comp4C2 making the former to be more nutrient-dense than the latter, therefore the maize variety, BR9928DMR-SY is recommended as more nutritious maize variety for consumption and processing into other maize products. Keywords—amylose content, functional properties, maize varieties, pasting properties, proximate composition.
ARTIFICIAL NEURAL NETWORK BASED MR BRAIN IMAGE SEGMENTATION IN THREE NEURODEGENERATIVE DISEASES

Brain tissue segmentation of MRI helps in the possibility of improved clinical decision making and diagnosis, and it also gives a new insight into the mechanism of the disease. Manual interaction is time consuming and it may be bias and variable. This automated scheme is done on different degenerative diseases, including Alzheimer’s disease (AD), Parkinson’s Diseases (PD) and Epilepsy (EP). On studying the segmented image, the reduction in the GM in the brain image indicates the presence of degenerative disease. Segmentation procedure was done with real time data. Automated segmented images were analyzed by the treating physician, by manually segmenting it. Accuracy, Sensitivity, Specificity and Youden index can be improved to a large extend by using the ANN technique. In this proposed study, we have investigated the classification from AD, PD and EP with ANN technique. Comparison of the results with three different study groups displays the promise of our approach. The contribution of this work is an approach for automatically segmenting the brain tissues into White Matter (WM), Gray Matter (GM), Cerebro-Spinal Fluid (CSF). The highest accuracy rates for the classification of AD were 96.13% and the classification accuracy of PD was 93.26% and classification of EP was 91.33% respectively. The algorithm developed achieves
Image Multi-Thresholding By Combining The Lattice Boltzmann Method and Level Set Method Of Active Contour for Image Segmentation Using Fuzzy Clustering

-During the last decades, the development of high dimensional large-scale imaging devices increases the need of fast, accurate and parallelizable segmentation methods. Due to its intrinsic advantages such as its ability to handle complex shapes, the level set method (LSM) has been widely used. In this paper, Lattice Boltzmann Method (LBM) has been proposed to simulate the well known active contour model (the CV model) for image segmentation. The proposed method provides a new numerical solution for solving the level set equation of the active contour model. As a local and explicit scheme, the algorithm based on LBM is not only stable with large steps, but also overcomes the difficulty in parallel computing of most implicit difference approaches. Experimental results demonstrate that LBM is computationally more efficient than the semi-implicit discrete method of CV model.
Image Processing Techniques for Hand Gesture and Sign Recognition

- Gesture is a distinct form of sign language which involves movement of body such as hands or face to express the meaning. Hand gesture has received a greater importance over the last few years because to remove the barrier of communication between mute people and normal people. It is an object consists of distinct features to extract and recognize the gestures or signs exactly, therefore gesture recognition presents a most difficult and challenging tasks in the fields of image processing, computer vision and image analysis. The images are subjected to image processing steps. The processing steps consist of image acquisition, pre-processing, segmentation, feature extraction and classifiers. In order to achieve a better accuracy the image processing and machine learning techniques and algorithms are used.
A new calculation formula of the nuclear cross-section of therapeutic protons

Purpose: We have previously developed for nuclear cross-sections of therapeutic protons a calculation model, which is founded on the collective model as well as a quantum mechanical many particle problem to derive the S matrix and transition probabilities. In this communication, we show that the resonances can be derived by shifted Gaussian functions, whereas the unspecific nuclear interaction compounds can be represented by an error function, which also provides the asymptotic behavior. Method: The energy shifts can be interpreted in terms of necessary domains of energy to excite typical nuclear processes. Thus the necessary formulas referring to previous calculations of nuclear cross-sections will be represented. The mass number AN determines the strong interaction range, i.e. RStrong = 1.2·10-13·AN1/3cm. The threshold energy ETh of the energy barrier is determined by the condition Estrong = ECoulomb. Results and Conclusion: A linear combination of Gaussians, which contain additional energy shifts, and an error function incorporate a possible representation of Fermi-Dirac statistics, which is applied here to nuclear excitations and reaction with release of secondary particles. The new calculation formula provides a better understanding of different types of resonances occurring in nuclear interactions with protons. The present study is mainly a continuation of published papers.1-3
MULTI IMAGE DEBLURRING USING COMPLEMENTARY SETS OF FLUTTERING PATTERNS BY MULTIPLIER METHOD

: Image deblurring is a big problem in image processing technique. In this project, we try to removing blurring items from images from different backgrounds. In almost all cases, there is insufficient information in the blurred image to uniquely determine a plausible original image, making it an ill-posed problem. In addition the blurred image contains additional noise which complicates the task of determining the original image. This is generally solved by the use of a regularization term to attempt to eliminate impossible solutions. And also the optimization problem is solved by transforming the original noisy image problem to an equivalent noiseless image. For this problem we propose a multiplier method is used to handle the restraints
ANALYSIS OF KNN, C5.0 AND ONE CLASS SVMFOR INTRUSION DETECTION SYSTEM

: Cyber security attacks have become increasingly sophisticated and complex. Intrusion detection is one of the major challenge in computer security that aims to detect unseen attacks in order to protect internal networks . Intrusion detection system compact with huge quantity of data which is a crucial task to keep good quality of features and remove the redundant and irrelevant features. An intelligent intrusion detection system combining ensemble classifier for classifying abnormal and normal actions in the computer network is presented. In this work, K-Nearest neighbourhood algorithm is used to determine the best optimal subset. The misuse detection model is built based on the C5.0 algorithm. Further, anomaly detection model is implemented by one-class Support Vector Machine (SVM) to detect the anomalies. Integration of ensemble algorithms helps achieve better classification. Based on the exhaustive analysis carried out, we propose that on incorporating, NSL-KDD Dataset, which is a well known benchmark for intrusion detection dataset, the system will result better in terms of anomaly detection rate, false positive, false negative, true positive, true negative, and f-score.
A Phy/Mac Layer Mechanism to Improve Aggregation in WLANs

- In recent years, the growth of WLANs (Wireless Local Area Networks) is very high and it has become the common means of access to the internet. The proliferation of Wi-Fi based WLANs/Wi-Fi hotspots in public places enables ubiquitous internet access. The public places are referred to as large audience environments, in which each Wi-Fi access point (AP) normally serves a crowd of mobile stations (STAs) simultaneously. The performance of those Wi-Fi hotspots is extremely poor in terms of low throughput and severe delay. After carefully investigating the traffic characteristics in Wi-Fi hotspots the studies shows that the main causes of such poor performance are media access control (MAC) inefficiency and downlink-uplink traffic asymmetry. To cope with these issues, the key idea is pooling, which facilitates, an access point (AP) to pool frames for multiple STAs in a single transmission. It reduces contention overhead in downlink transmission and conveys more frames in each channel access. As such, each downlink transmission carries more payloads for multiple receivers, it enables in time response for multiple STAs concurrently in a single transmission and thus improves MAC efficiency and solves traffic asymmetry simultaneously.
STANDARDIZATION OF AN AYURVEDIC FORMULATION: TRIKATU CHURNA USING BIOANALYTICAL TOOLS

Trikatu Churna (TC) is an ancient traditional Ayurvedic preparation prescribed for a wide range of disorders. Though TC is an age old formulation, there are very few references on its quality control and standardization. In this work, an attempt has been made to standardize TC by qualitatively evaluating the preliminary phytochemicals. Piperine content of TC was determined using HPTLC. Evaluation of safety potential of TC samples and stability evaluation by comparative study of the in house TC formulation with marketed TC formulations with respect to their piperine content is a value addition to the current work.
EFFECT OF SIGNAL COMPOUNDS ON ANDROGRAPHOLIDE IN THE HAIRY ROOT CULTURES OF ANDROGRAPHIS PANICULATA

E-mail: r_sharmi2k3@yahoo.co.in ABSTRACT: Andrographolide is one of the main active principles in Andrographis paniculata . It exhibits enormous pharmacological effects. Due to this immense value it required more level in pharmaceutical to satisfy the global need. Because it present in a very low level in normal regenerated plant of Andrographis paniculata. Micro propagation is a proven method for efficient invitropropagation of medicinal plants and for commercial exploitation of valuable plant derived pharmaceuticals. Hence in this present study aimed that to enhance the andrographolide content through hairy root culture techniques and elicitation process. Hairy root cultures induced in Andrographis paniculata used for determining the effect of signal compound on andrographolide production. Methyl jasmonate and salicylic acid were used in varying concentration and introduced into the 10d old hairy root cultures. HPLC studies are revealed that methyl jasmonate at 100 mM concentration shows higher efficacy (5fold/25d) than other increasing concentrations of same. Salicylic acid shows 8 fold (100mM) enhancements than hairy root cultures (control).
Inclusion of Flow Management for Automatic and Dynamic Route Discovery System by ARS

— This paper presents an autonomous network reconfiguration system (ARS) that enables a multiradio WMN to autonomously recover from local link failures to preserve network performance. These failures cause severe performance deprivation in WMNs or require expensive manual network management for their real-time recovery. By using channel and radio diversities in WMNs, ARS generates necessary changes in local radio and channel assignments in order to recover from failures. Next, based on the thus generated configuration changes, the system considerately reconfigures network settings among local mesh routers. Multihop wireless Mesh Networks (WMN) experience frequent link failures due to channel interference, dynamic obstacles and bandwidth demands. They guide to performance deprivation in wireless mesh networks. WMNs are developing and deploy for public safety, environment monitoring and city wide wireless internet services.
Analysis of Lung Nodule and Classification based on SVM Classifier and Fuzzy C-Means Clustering for Effective Segmentation

— Lung cancer is the most type of cancer among all the cancer with less survival rate. It is very difficult to analyze the cancer at its early stage. In this paper, using computed tomography CT images. we proposed a classification method for the four types of lung nodules, i.e., well-circumscribed, vascular-ized, juxta-pleural and pleural-tail using SVM classifier and fuzzy C-means clustering analysis for segmentation and feature extraction with the help of DWT and MWT transform and LBP for the detection of cancerous cells.