Privacy Preserving Data Mining by Using Implicit Function Theorem

Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge of information extracted from data mining system to be shared by the intended persons not to everyone to access. In this paper, we proposed a new approach of privacy preserving data mining by using implicit function theorem for secure transformation of sensitive data obtained from data mining system. we proposed two way enhanced security approach. First transforming original values of sensitive data into different partial derivatives of functional values for perturbation of data. secondly generating symmetric key value by Eigen values of jacobian matrix for secure computation. we given an example of academic sensitive data converting into vector valued functions to explain about our proposed concept and presented implementation based results of new proposed of approach

Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art on several metrics.Comment: Presented at 2nd ML for PHM Workshop at SIGKDD 2017, Halifax, Canad

Secure and Energy Aware Cluster based Routing using Trust Centric – Multiobjective Black Widow Optimization for large scale WSN

Wireless Sensor Network (WSN) is a promising approach that is developed for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. Moreover, an inappropriate selection of routing path affects the data delivery of the WSN. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, Trust Centric - Multiobjective Black Widow Optimization (TC-MBWO) is proposed for selection of Secure Cluster Head (SCH) from the large-scale WSN. Moreover, the secure routing path is generated by using the TC-MBWO, in which the factors considered for the cost function are: residual energy, distance, trust and node degree. Therefore, the secured clustering and routing achieved by using TC-MBWO, provides the resistance against malicious nodes and simultaneously the energy consumption is also minimized by identifying the shortest path. The proposed TC-MBWO method is analyzed in terms of alive nodes, dead nodes, energy consumption, throughput, and network lifetime. Here, the TC-MBWO method is compared with different existing methods such as Low Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm Optimization - Grey Wolf Optimizer (PSO-GWO), Particle-Water Wave Optimization (P-WWO) and Particle-based Spider Monkey Optimization (P-SMO). The alive nodes of the TC-MBWO are 70 for 2800 rounds which are higher in number when compared to the PSO-GWO, P-WWO and P-SMO

DESIGN AND IN VITRO EVALUATION OF FLOATING DRUG DELIVERY SYSTEM OF GLIPIZIDE USING COMBINATION OF NATURAL MUCILAGES AND SYNTHETIC POLYMERS

Objective: The objective of the study is to explore polysaccharide mucilages of Colocasia esculenta (CE), and Fenugreek (FG) as buoyancy enhancing agents, and mucoadhesive agents by developing gastroretentive floating tablets of Glipizide. Methods: Glipizide loaded floating tablets were developed with CE, and FG alone and in combination of Guar gum (GG), and Hydroxypropyl methylcellulose (HPMC) K4M using direct compression technique. The developed formulations have been subjected to evaluation of in vitro buoyancy study, in vitro drug release study (pH 1.2), and in vitro bioadhesiveness study. Therefore, the final optimized formulation was subjected to Fourier Transform Infrared Spectroscopy (FTIR), Differential scanning calorimetry (DSC), and X-ray powder diffraction (XRD) study. Results: The results of the buoyancy study for formulation F1, F2, and F5 revealed that the instant floating lag time, floating time duration of 1 h, and exhibited 100% drug release in 4 h. Therefore, the formulations developed with GG (F3), and HPMC K4M (F4) have been exhibited slow floating lag time, prolonged floating duration and drug released up to 100 % in 12 h, while; formulations F6, F7, F8, and F9 have been exhibited shortest floating lag time, longest floating time duration, the best drug released up to 12 h, and better in vitro bioadhesiveness properties. Furthermore, F7 exhibited good bioadhesive property as compared to F6, F8-F9. The results of the FTIR, DSC, and XRD study for F7 revealed that the presence of functional groups and amorphous. Conclusion: Owing to the anticipated properties like biocompatibility, biodegradability, swelling ability, and cost-effectiveness of CE; it could be the potential macromolecule for the replacement of synthetic polymers

Deep Learning Convolutional Neural Networks for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) has received an excellent deal of interest within the researchcommunity. A CBIR system operates on the visible features at low-level of a user's input image that makes it troublesome for the users to devise the input and additionally doesn't offer adequate retrieval results. In CBIR system, the study of the useful representation of features and appropriate similarity metrics is extremely necessary for improving the performance of retrieval task. Semantic gap has been the main issue which occurs between image pixels at low-level and semantics at high-level interpreted by humans. Among varied methods, machine learning (ML) has been explored as a feasible way to reduce the semantic gap. Inspired by the current success of deep learning methods for computer vision applications, in this paper, we aim to confront an advance deep learning method, known as Convolutional Neural Network (CNN), for studying feature representations and similarity measures. In this paper, we explored the applications of CNNs towards solving classification andretrieval problems. For retrieval of similar images, we agreed on using transfer learning to apply the deep architecture to our problem. Extracting the last-but-one fully connected layer from the retraining of proposed CNN model served as the feature vectors for each image, computing Euclidean distances between these feature vectors and that of our query image to return the closest matches in the datase

HMM Based Text-to-Speech Synthesis for Telugu

This thesis describes a novel approach to build a general purpose working Telugu text-to- speech synthesis system (TTS) based on hidden Markov model (HMM) which is reasonably intelligible, natural sounding and exible. There have been several attempts proposed to use HMM for constructing TTS systems. Most of such systems are based on waveform concatenation techniques. To fully convey information present in speech signals, text-to-speech synthesis systems are required to have an ability to generate natural sounding speech with arbitrary speakers individualities and emotions (e.g., anger, sadness, joy). To represent all these factors the Mel- cepstral coefficients are extracted as spectral parameters. Excitation parameters are extracted using fundamental frequency(F0)

Performance Evaluation of Bio-Medical Waste Incinerated Ash and Cement Blend as Stabilizing Agent for Low Volume Road Bases

Incineration is the most commonly employed alternate disposal strategy of biomedical waste across the globe, which produces Biomedical Waste Incinerated Fly Ash (BMWIFA). BMWIFA is often disposed of in landfills to prevent environmental contamination. Due to limited space and the high cost of land disposal, recycling methods and ash reuse in various systems have been developed. Therefore, the present study evaluates the performance of BMWIFA and Ordinary Portland Cement (OPC) blends as stabilizing agents for the base layers of low-volume roads (LVRs). Different trial mixes of crushed aggregate (CA), BMWIFA, and OPC were tested to find the optimum mix. The stabilizer content was considered to be 3.0%, 5.0%, and 7.0% of the total dry weight of the mix, in which the BMWIFA (a)/OPC (c) ratio is taken as 100/0, 80/20, 60/40, 40/60, 20/80, and 0/100 in each percentage of stabilizer. Optimum values of compaction characteristics were used for strength evaluations of mixes in terms of unconfined compressive strength (UCS) and indirect tensile strength (ITS) at 7, 14, and 28 days of air curing. The mix proportions 97% CA, 95% CA, and 93% CA stabilized with 3% (a/c = 20/80), 5% (a/c = 40/60), 7% (a/c = 60/40) binders respectively, satisfied the 7-day UCS requirements (3MPa) according to the Ministry of Rural Development (MoRD) for LVR cement-treated bases and were found durable. Furthermore, the Toxicity Characteristics Leaching Procedure (TCLP) analysis for various heavy metals reveals that the CA, BMWIFA, and OPC compositions were non-hazardous materials. Finally, this study's findings recommend the use of BMWIFA and OPC blends as stabilizers in low-volume road construction

Trends in Photopolymerizable Bioinks for 3D Bioprinting of Tumor Models

Three-dimensional (3D) bioprinting technologies involving photopolymerizable bioinks (PBs) have attracted enormous attention in recent times owing to their ability to recreate complex structures with high resolution, mechanical stability, and favorable printing conditions that are suited for encapsulating cells. 3D bioprinted tissue constructs involving PBs can offer better insights into the tumor microenvironment and offer platforms for drug screening to advance cancer research. These bioinks enable the incorporation of physiologically relevant cell densities, tissue-mimetic stiffness, and vascularized channels and biochemical gradients in the 3D tumor models, unlike conventional two-dimensional (2D) cultures or other 3D scaffold fabrication technologies. In this perspective, we present the emerging techniques of 3D bioprinting using PBs in the context of cancer research, with a specific focus on the efforts to recapitulate the complexity of the tumor microenvironment. We describe printing approaches and various PB formulations compatible with these techniques along with recent attempts to bioprint 3D tumor models for studying migration and metastasis, cell-cell interactions, cell-extracellular matrix interactions, and drug screening relevant to cancer. We discuss the limitations and identify unexplored opportunities in this field for clinical and commercial translation of these emerging technologies

VIDEO MONITORING SYSTEM BASED ON ARM9 CORE IN CLIENT-SERVER MODEL

In this paper, Embedded Real-time video monitoring system based on ARM9 is designed, in which the embedded chip and the programming techniques are adopted. It focus the hardware composition and the methods of realization software modules.  First, USB camera video data are collected by the embedded Linux system, processed, compressed and transferred by the processing chip. Then, video data are sent to the monitor client by LAN network. In the client-server model and Linux Input sub system, and develop a driver and tested it. By using Input subsystem, it can control the LED’s flashing types and rates on the target board through the network.  Embedded Linux is chosen an operating system which provides open-source, multi-task, multi-process, highly modular, multi-platform support, performance and stability to the system

Comprehensive Characterization of Soy Protein, Cowpea, Moth Bean and Wheat Gluten as Functional Ingredients for the Development of Plant-Based Extruded Meat Analogues

The production of meat analogs using low-moisture extrusion is a complex process influenced by the composition of raw materials, processing parameters, and equipment design. Achieving a meat-like texture depends on the physicochemical and functional properties of the ingredients, which directly affect extrusion behavior and final product quality. This study aimed to evaluate the characteristics of various protein-rich plant-based raw materials are soy protein isolate (SPI), defatted soy flour (DFS), cowpea flour (CPF), moth bean flour (MBF), and wheat gluten (WG) for their suitability in meat analog production. The materials were analyzed for proximate composition (moisture, carbohydrate, protein, fat, ash, and dietary fiber), bulk density, water and oil absorption capacity, solubility indices, particle size distribution, and viscosity. The results provide valuable insights for optimizing raw material selection and processing conditions to improve extrusion performance and enhance the texture and quality of plant-based meat analogs