Turkish Journal of Computer and Mathematics Education (TURCOMAT)

The Keyframe based Face in Video Recognition using Multi-Artificial Neural Network

DR. S. WILSON 528 — Wed, 16 Oct 2024 00:00:00 +0000

Face in video recognition has several challenges. Although deep learning approaches have achieved performance that surpasses people for still image- face recognition, video- face recognition remains a challenging task due to the large volume of data to be processed and intra / intervideo variations in pose, lighting, occlusion, scene, blur, video quality, etc. In this paper, deep convolutional neural network is used for feature extraction and artificial neural network is used for face recognition. The computation overhead of these deep learning approaches is reduced by introducing keyframe based face recognition in video. The low-quality frames are removed by extracting keyframes. The proposed method is tested on YTF dataset and the results are compared with recent methods. The experimental results substantially proved that the proposed method achieves a higher accuracy rate of 98.36% when compared with other recent methods.

A Review on Methods used to determine Fractal Dimension Analysis of AFM Images of Thin Film

Priyanka Singh — Wed, 16 Oct 2024 00:00:00 +0000

This aims the applications in the field of surface analysis and study of structure of different materials. The purpose
of this examination is to additionally research the ultra-structural details of the surface of thin film using atomic
force microscopy (AFM) images. The fractal dimension, gave quantitative qualities that describe the scale
properties of surface geometry. Detailed identification of the surface geometry was obtained using statistical
parameters. The analyzed AFM images confirm a fractal nature of the surface, which is not taken into account by
classical surface statistical parameters. In this paper, we present a review on different methods used to determine
fractal dimension of AFM images of thin film.

Investment Objective, Investment Portion and Risk Taking Capacity of the Respondents in Agricultural Commodity Market of Narmadapuram Division with Reference to Soyabean and Wheat Crop

Apeksha Daley Dubey, R. K. Patil — Wed, 16 Oct 2024 00:00:00 +0000

Narmadapuram division consists of Harda, Betul and Hoshangabad district and they are chosen for the present study as per stratified random sampling. The said districts were selected because Soyabean and Wheat crop are cultivated at large while comparing with other districts. The study pertaining about the commodity market covers the period of 3 years (2014-2016). In the present study the investment objectives, investment portion and risk taking capacity of the respondents in agricultural commodity market of Narmadapuram division.

SUB CLASS OF HARMONIC UNIVALENT FUNCTIONS WITH INTEGRAL OPERATOR

Nagalaxmi Nakeertha,V. Srinivas — Wed, 16 Oct 2024 00:00:00 +0000

This paper introduced a new class of harmonic univalent function defined by an integral operator. Additionally study investigates some properties of this subclass such as essential as well as adequate coefficient bounds, extreme points, distortion bounds and hadamard product.

S-METRIC SPACES FIXED POINT RESULTS ON ALTMAN INTEGRAL TYPE MAPPINGS

M.L.L.Phanikanth, T.Siva Krishna, M.Vijaya Kumar — Wed, 16 Oct 2024 00:00:00 +0000

In this present article, we establish the concept of

Common Fixed Point Theorems Satisfying Contractive Type Conditions in Complex Valued Metric Spaces

Uma. M — Wed, 16 Oct 2024 00:00:00 +0000

In this paper we prove common fixed point theorems satisfying contractive conditions involving rational expressions and product for four mappings that satisfy property (E.A) along with weak compatibility of pairs are proved property are obtained in complex valued metric spaces which generalize various results of ordinary metric spaces

Performance Analysis of Big Data with Data models using Artificial Intelligence

L.Umarani, Dr. A. John Sanjeev Kumar — Wed, 16 Oct 2024 00:00:00 +0000

Proliferation of new information sources such as medical images, financial data, sales data, radio frequency identification and web tracking data, there is a challenge to decipher trends and gain sense of data that is orders of magnitude larger than ever earlier. One of the technologies most often associated with the era of big data is Hadoop. Although in that respect is much expert information about Hadoop, there is not much info around how to effectively structure data in a Hadoop environment. Though the nature of parallel processing and the MapReduce system provide an optimal environment for processing big data quickly, the structure of the big data itself plays a vital role. This paper explores doable used for data modeling in a Hadoop environment. Specifically, the purpose of the experiments described in this paper was to figure out the best structure and physical modeling techniques for storing data in a Hadoop cluster using Hive to enable efficient data access. Although other software interacts with Hadoop, the experiments focused on Hive. The Hive infrastructure is most felicitous for traditional data warehousing-type applications. The experiment does not focus on HBase. This paper explores a data partition strategy and investigates the role indexing, data types, file types, and other data architecture decisions play in designing data structures in Hive. To test the different data structures, it focused on typical queries utilized for analyzing web traffic data. These test included most referring sites, web analyses such as counts of visitors, and other typical business questions used by weblog data. The primary measure for culling the optimal structure of data in the Hive is predicated on the performance of web analysis queries. For comparison purposes, it was quantified the performance in Hive and the performance in an RDBMS. The reason for this comparison is to more preponderant understand how the techniques that we are habituated with utilizing in an RDBMS work in the Hive environment. It explored techniques such as storing data as a compressed sequence file in Hive that are particular to the Hive architecture. Through these experiments, it endeavored to show that how data is structured (in effect, data modeling) is just as consequential in an immensely colossal data environment as it is in the traditional database world.

On nearly C-compact space

Gopal Adak — Wed, 16 Oct 2024 00:00:00 +0000

In this paper we have introduced a new type of convex topological space [5]
called nearly C-compact space where a convex topological space is a topological space with a convexity [16]. Some fundamental characterizations and various basic properties have been obtained. Its relationship with other types of compact spaces is also investigated

Taguchi Analysis of Pervious Concrete Mixtures: A Way to Increase Strength and Permeability

Prerana R. Ikhar — Wed, 16 Oct 2024 00:00:00 +0000

A unique variety of concrete called pervious concrete is created by combining water, cement, and open-graded coarse particles. Usually, it contains very little to no fine aggregate concrete and only enough cement paste to coat the aggregate particles while preserving the interconnectedness of the spaces. The terms porous concrete, permeable concrete, no fines concrete, gap graded concrete, and improved porosity concrete are also used to describe pervious concrete. The experimental technique and findings for compressive strength, flexural strength, and permeability are presented in this work. Using Taguchi analysis, we were able to create an experiment with three variable factors—the mix proportion, the percentage of fine aggregates, and the percentage of human hairs as fibers—each with three levels. L9 arrays were utilized in this experiment. While varying the proportions of human hair as fibers of fine aggregate with coarse aggregates from 0.25%, 0.50%, and 0.75% of human hair of 0%, 5%, and 10% of fine aggregate accordingly in each proportion, the w/c ratio of 0.4 was used in this study. Whose findings show that the maximum compressive strength of M9 mix is between 1.45 and 3.48 N/mm2, the maximum flexural strength of M9 mix is between 0.135 and 2.11 N/mm2, and the maximum permeability of M8 mix is between 91.67 and 163.70 M/hr. That goes to show that adding more fibers helps to boost flexural strength, while adding more fine aggregates increases compressive strength at the same time.