Miracle-dz/UniFAR-FTUs huggingface dataset:
A BACKPROPOGATION NEURAL NETWORK FOR THE PREDICTION OF EARTHQUAKE IN JAPAN

This paper presents a BackPropogation neural network for predicting earthquake in Japan with proper data regarding the earthquakes. These parameters are taken from Japan seismic catalogue included all minor, significant occasions and aftershock sequences in Japan. This information contains event information, time of the event, latitude, longitude, magnitude, root mean square and depth of the earthquake. These information are grouped together to form seismicity indicators , It is then used as inputs for BP neural network for learning and predicting the magnitude of earthquake. These scientifically registered indicators considered are grouped based on each events above 3.5 range , add up to number of occasions from 2010 to 2016, b-values, energy released from the occasion, Gutenberg-Richter inverse power law curve for n events. We propose a three-layer feed forward BP neural network model to learn with the real event of the earthquake magnitude M and other numerical seismicity indicating parameters as information and target vectors in Japan range. We gather through looking at bend as observed from seismometer in Japan list contained all occasions above greatness 3.5 mg, their aftershock sequences in the Japan of year 2016 and BP neural network predicting earthquakes in 2016.
Bi-Similarity Mapping Based Image Retrieval Using Shape Features

Shape based image retrieval is a very active research area in image processing. Effective and efficient retrieval of similar images from large image databases is still a challenging problem inspite of the high relevance that shape information can have in describing image contents. The goal is to develop an effective retrieval system to overcome a few limitations associated with existing systems. This paper proposes Co-Transduction algorithm that is a shape based image retrieval algorithm, which is to combine different similarity measures for robust shape based image retrieval through a semi supervised learning framework. The accuracy of adopted similarity measures (distances or metrics) decides the performance of a retrieval system. In shape based image retrieval, intra-class shapes should have smaller distances than inter-class shapes. Given two similarity measures and a query shape, the algorithm iteratively retrieves the most similar images using one measure and assigns them to a pool for the other measure to do a reranking, and vice versa.
Evaluation of MIMO for Indoor Channels with Spatial Modulation

Currently Modeling Indoor channel is an active research area and is used in a wide variety of applications such as voice over IP (VoIP), streaming video and music and network attached storage. These applications use MIMO for controlling the IR based system using LED’S and PD’S.In Multiple-Input Multiple-Output (MIMO) systems, a multiple-stream transmission takes place between a base station (BS) and a Mobile Stations (MS). This includes inter-stream interference (IStI). To overcome IStI,a novel technique or system can be proposed . The proposed system is based on E-SDM scheme is used for suppressing IStI. Along with this technique spatial modulation method is used to boost up the spectral efficiency and achieves the multiplexing gain by activating a single transmit antenna in each time slot. In practical scenario, channel will change over time which creates the interference in the MIMO system usually. A new VLSI based Spatial Modulation technique will be proposed. In the new technique, the distance between the transmitter and receiver is measured along with total length and breadth of the room which divide the room into different blocks using Block Diagonalization (BD) scheme which eliminate the inter-user interference and Additive White Gaussian Noise (AWGN). To make efficient time slots, achieve maximum gain and spectral efficiency, Spatial Modulation (SM) transmission scheme is used after multiplexing. To verify the performance of the channel prediction scheme in communication environments a large amount of channel data is measured.Using these data, we examine the channel transition and channeltracking with the prediction method. ABit-Error Rate (BER) performance is measured to evaluate this result and Average Bit Error Probability (ABEP) is also calculated which measures the system configuration and it is compared with the existing technique to confirm its efficiency towards Multi-User MIMO Systemwith Prediction of Time-Varying indoor channel. At the receiver side, receive the encoded data and decode it. Keyword: Multiple-Input Multiple-Output (MIMO), Bit-Error Rate (BER), Average Bit Error Probability (ABEP), Spatial Modulation.
Anti-diabetic Effect of Neem and Spirulina in Alloxan Induced Diabetic Mice

In this study the anti-diabetic effects of neem and spirulina were evaluated in alloxan-induced diabetic mice. After acclimatization, 25 Swiss Albino male mice (Mus musculus) were randomly divided into five equal groups, from which one was kept as control (C) and other 4 groups of mice were induced diabetes by single intraperitoneal injection of alloxan @ 240 mg/kg body wt. For therapeutic justification, out of 4 alloxan-induced diabetic groups, one was kept as diabetic control (DC) whereas other three groups were treated with oral administration of 500 mg neem leaves/kg body wt (DN), 20 mg/kg body wt spirulina (DS) and combination of neem extract plus spirulina (DNS), respectively, for 42 consecutive days. All three treated groups of mice such as DN, DS and DNS significantly (P<0.001) reduced the up regulated blood glucose, total plasma cholesterol such as LDL and triglyceride (TG). Besides these, the mice of group DN and DNS showed significant (P<0.001) increase of erythrocytes and decrease (P<0.05) of total leukocyte count. The highest body weight gain was found in the mice of group DN (P<0.05). We, therefore, conclude that crude extract of neem leaves and/or spirulina could be an effective alternative therapy for diabetic patients.
Elevating Data Integrity Techniques in Matching Data Sets for Web Databases

— The information Deduplication task has attracted a great deal of attention in the research community to be able to provide efficient and effective solutions. The data supplied by the consumer to tune the Deduplication process is generally symbolized by some hand labeled pairs. Most condition-of-the-art record matching methods are supervised, which necessitates the user to supply training data. These techniques are not relevant for that Web database scenario, in which the records to complement are query results dynamically generated on the-fly. Such records are query-dependent along with a pre-learned method using training examples from previous query results may fail around the outcomes of a brand new query. In large datasets, producing this sort of labeled set is really a daunting task because it requires a specialist to pick and label a lot of informative pairs. Previous all retrieval models and diversification techniques does not solve the ranking problems. Manifold ranking with sink points is among the novel technique or approach. Within this novel approach ranking troubles are present. Users won't be satisfied with present manifold ranking results. Beginning with the no duplicate set, we use two cooperating classifiers, a weighted component similarity summing classifier as well as an SVM classifier, to iteratively identify duplicates in the query result records of multiple Web databases. We produce an without supervision, online record matching method, UDD, which, for any given query, can effectively identify duplicates in the query result records of multiple Web databases. We advise an approach to produce balanced subsets of candidate pairs for labeling. Within the second stage, an energetic selection is incrementally invoked to get rid of the redundant pairs within the subsets produced within the first stage to be able to provide an even smaller sized and much more informative training set. New technique extracts the efficient manifold results rival all previous techniques and takes away the noisy objects.
An Energy–Efficient Heuristic based Routing Protocol in Wireless Sensor Networks

— Energy consumption is the critical issue to determine the lifetime of Wireless sensor networks (WSNs), due to limited batteries power resource of sensor nodes. The optimization of energy consumption is involves reduction of energy consumption in order to prolong the lifetime of the network as much as possible. However, to minimize the overall energy consumption of the sensor network, different types of routing protocols have been proposed. This paper proposes an energy efficient routing protocol using the A-star search algorithm to find the optimal path from the source node to the sink node. Simulation results with Matlab show that our proposed protocol is efficient in terms of total energy consumption and network lifetime compared with fuzzy approach.
A SURVEY ON DETECTION OF BOTNET IN MOBILE PLATFORM

: Botnets are the main threat to the security of the devices, Traditionally Botnets are in the desktop platform ,There are different proposed techniques to detect them in the desktop platform and obtained good result in that techniques, When coming to the mobile platform the Botnets are new to this domain and they will increase rapidly in future days, Now a day’s mobiles are using like a small pc’s we are doing money transactions in mobiles and many confidential information are stored in the mobile, So providing security to the mobile platform is necessary by detecting the botnets, To detect the botnet in mobile platform already some researchers are proposed the different techniques to detect them, So in this paper we will going through a survey on different proposed techniques to detect the Botnets on mobile platform.
Intelligent Energy Aware Routing (IERA) Algorithm based on Dynamic Sink Nodes

The emergence of big data and cloud technology has driven a fast development of wireless sensor networks (WSNs) [1–4]. A sensor node is normally comprised of one or more sensor units, a power supply unit, a data processing unit, data storage, and a data transmission unit [5]. A wireless sensor network is a collection of wireless nodes with limited energy that may be mobile or stationary and are located randomly in a dynamically changing environment. Wireless sensor networks hold the promise of revolutionizing the way we observe and interact with the physical world in a wide range of application domains such as environmental sensing, habitat monitoring and tracking, military defence, etc. [6]. A variety of disciplines uses the WSNs for numerous applications like monitoring of specific features or targets especially in rescue and surveillance applications, medical, engineering and industrial applications and many more. As they are easy to handle because of the small size and is of affordable cost the use of such sensors became common. Besides that they can be deployed in areas like underground, underwater or the normal landscape which made it more feasible. Now a day’s everywhere we can see the surveillance equipments which are deployed with sensors. Smart home environments are yet another application. Lot many applications in medical field use WSNs like patient monitoring. Target tracking and war zones extensively rely on these. The characteristics of low-cost, low multifunctional sensor have attracted a great deal of research attention, in that sensor nodes can perform intelligent cooperative tasks under stringent constrains
A Hybrid Translator: From Malayalam to English

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Machine translation is a process of translation from one language to another. The language from which we are translating is called the source language and the language to which we are translating is called the target language. So machine translation can be defined as a process of translation from a source language to a target language. Here the source language is chosen as Malayalam and the target language is English. Statistical based approach towards machine translation is commonly used for the translation purpose because it is superior to any other approaches. But due to the peculiarity nature of Malayalam it is not a good practice to develop a translator for this language by using the statistical method. Hence we are integrating some of the rules for the translation purpose which helps the system to understand how to translate from Malayalam in an easier way. Here we are proposing a translator which can translate from Malayalam to English by combining a rule based and a statistical based approache, hence it is a hybrid translator.
Evaluation of the Physiological Specialization of the Hearing Organs of Posthatch Chicken Rearing at Different Environmental Circumstances

1 Research Scholar, Department of Zoology, P.M. B. Gujarati Science College, Devi Ahilya Vishwavidyalaya,Indore, (M.P.) India. 2 Ph.D. Research Scholar, Department of Zoology, Mata Jija Bai Govt. Girls P.G. College, Moti Tabela, Devi Ahilya Vishwavidyalaya, Indore, (M.P.) India. 3 Professor & Head of Zoology Department in P.M. B. Gujarati Science College, Devi Ahilya Vishwavidyalaya Indore, (M.P.) India Email mangilalmaida@gmail.com sandeepcharpota@gmail.com
Resource Efficient Wireless Sensor Networks for Temperature and Gas Monitoring

Health and safety plays an important role in everyday life. Inhaling poisonous gas may lead to harmful effect; hence monitoring the air quality is essential. Wireless Sensor node can monitor temperature and air quality, which helps us to avoid the disaster. The main problem with Wireless Sensor node is that it consumes more energy for monitoring the environment. Existing system reduces the power consumption by making the node to switch between sleep and wake state. During the sleep state, the power consumption of the sensor is negligible and the power consumed by the sensor node is reduced. Existing system does not consider the occurrence of collision of the signals during transmission. A hybrid technique is proposed based on the priority, aggregation and scheduling of data to avoid the collision of signals during transmission. This reduces the energy consumption of the Wireless Sensor nodes by accumulating the high priority signals together and transmits without collision. The proposed work is used to monitor the air quality and temperature with reduced power consumption. An alert message is also generated and transmitted to the user in case of emergency.
Implementation of Multilayer AHB Busmatrix for ARM

— The multi-layer AHB busmatrix (ML-AHB busmatrix) proposed by ARM is a highly efficient on chip bus that allows parallel access paths between multiple masters and slaves in a system. However, the ML-AHB busmatrix of ARM offers only transfer-based fixed-priority and round-robin arbitration schemes. In this paper, we present one way to improve the arbiter implementation of the ML-AHB busmatrix. The proposed arbiter, which is Self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level and the desired transfer length from the masters so that arbitration leads to the maximum performance. Our SM arbitration scheme has the following advantages: 1) It can adjust the processed data unit; 2) it changes the priority policies during runtime; and 3) it is easy to tune the arbitration scheme according to the characteristics of the target application.
Sentiment Analysis on Demonetization using SVM

— Sentiment Analysis is an area of interest over the last decade. The social networking is one of the important sources for users to know express the views on different organizations, product, and politics. In this work, we focus on mining sentiments and analyzing public review on demonetization. Demonetization was one of the biggest political decisions taken in year 2016 which affected each and every person in India. In demonetization 500 and 1000 rupees currency was banned more over a new 2000 rupees note was introduced in currency. This affected economy, market and exposed black money also. We worked on twitter data for demonetization. It aims to analyzing positive and negative of tweets reviews as sentiment classification task. The raw dataset collected is preprocessed by cleaning unwanted text, tokenized and used for polarity classification of data corpus.
Ultimate Prediction of Stock Market Price Movement

Investment in the stock market is currently very popular due to its economic gain. Numerous researchers’ and academicians’ work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies’ data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.
Image Fusion using Discrete Wavelets Transform

— This paper explains brief algorithm of image fusion using discrete wavelet transform, Where spatial and spectral both information were preserved in the fused image. We have developed a robust technique to merge two multi focused images in to the one using wavelet transform. Image fusion achieved with DWT lifting scheme proves to be better than existing Image fusion techniques.
Selection for High Yield and Stability among Introduced Early Maturing Soybean Genotypes, Tested in Ethiopia for two qonsiquetive years across six testing locations

Abstract – Soybean [Glysin max (L.) Merrill] is an important source of proteins, oil and micronutrients to small holder farmers in North East and Western parts of Ethiopia, but the biggest challenge on its production is climate change and most of our high yielding soybean were late in maturity and constrained by rain fall. Therefore to overcome such problem, 14 soybean genotypes were introduced from (IITA) and evaluated at three locations for two years using RCBD design in three replication to identify high yielding and stable early maturing variety. Yield performance data was subjected to analysis of variance (ANOVA) using SAS 9.3version and Meta-R version 4.0 to test the significance of genotype × environmental interactions and stability analysis using yield-stability statistic(YSi) and GGE biplot for selection of high yield and stability. Following the detection of significant genotype × environmental interactions, yield stability statistics (YSi) were used for simultaneous selection for high yield and stability. Yield-stability statistics (YSi) indicated that among 14 introduced genotypes (TGX-1990-21F and TGX-1990-55F) where identified as high yielding and stable compered to local check. These genotypes need to be assessed for farmer preferences/tastes and other quality traits in on-farm participatory trials before they can be recommended for release.
Trends, Patterns and Processes of Information Management by the Nigerian Army in the United Nations Peace Support Operations in Liberia

Nigeria has been involved in the United Nations Peace Support Operations (UNPSOs) since independence. Nigeria’s Peacekeeping Operations with the direction of the trends, patterns and processes of information acquisition and the Nigerian Army’s (NA) Information Management in the UNPSO in Liberia were studied. The study adopted the Cohen’s Agenda Setting and employed a triangulation research design. Twentyeight officers and 172 soldiers were purposively sampled from Nigerian Battalions (NIBATTs) 36 and 37 that served in Liberia while 18 in-depth interviews were conducted with heads of military units in Liberia. Two key informant interviews were conducted with one Commanding Officer and the Military Assistant to the Force Commander. Secondary data were collected from Nigerian Army Signal Messages, Part One and Part Two Orders and the United Nations documents. Quantitative data were analysed using descriptive statistics, while qualitative data was content analysed.The critical role of information management in decision making was reflected by 192 (96.0%) of respondents who affirmed that adequate information management was crucial to the success of Peace Support Operations (PSOs) in Liberia. Also, 182 (91.0%) of the respondents averred that Information Management processes in NA PSOs in Liberia included communication links, satellites, cables and procedures, formats and filters for information transport and retrieval. Furthermore, 184 (92.0%) of the respondents held that trends and patterns of information on the NA in PSOs in Liberia was well managed and protected by both officers and soldiers. Information acquisition processes, trends and patterns of information management by Nigerian Army in PSOs in Liberia were in form of open sources like visual observations by patrol and authorised flights, open communication with belligerent forces and local populations while the act of cryptography or sending coded messages was employed as patterns for information sharing. Though beset with information sharing and coordination challenges, strategic information management by the Nigerian Army contingent enhanced operational efficiency and contributed to the overall success of the United Nations Peace Support Operations in Liberia. However, to achieve a more efficient and reliable management of information resource in PSOs, synergy on information management trends, patterns and processes among the different military battalions in the PSO should be
FIR Filter Implementation Based On MCM Technique Using Wallace Multiplier and Carry Save Adders

-The complexity of the digital filters usually depends on the number of adders which are used to implement a multiplier. In this project we want to use Multiple constant multiplication (MCM) technique to reduce the number of operations for implementing the multipliers. This MCM technique is implemented using Wallace multiplier and carry save adder. Implementation of FIR filters using Multiple constant multiplications (MCM) a is an efficient way of implementing several constant multiplications with the same input data. The coefficients of multiplier are expressed using shifts, adders, and subtractions. Introducing the Wallace tree multiplier will reduce both the number of adders and subtractions as well as the number of shifts. Multiple constant multiplication/accumulation in a direct FIR structure is implemented by means of an enhanced version of Wallace multipliers. In order to reduce the hardware which ultimately reduces an area and power, Energy Efficient full adders plays an important role in Wallace tree multiplier. The proposed Wallace multiplier will have fewer adders than Standard Wallace multiplier. In both multipliers, at the last stage Carry Save Adder (CSA) is used.