AIJREAS VOLUME 8, ISSUE 02 (2023, FEBRUARY)aerfpublications2023-02-14T05:38:54+00:00
AIJREAS
VOLUME 8, ISSUE 2 (2023, FEBRUARY)
(ISSN-2455-6300) ONLINE
ANVESHANA’S INTERNATIONAL JOURNAL OF RESEARCH IN ENGINEERING AND APPLIED SCIENCES
1.
A STUDY OF MATHEMATICAL INFLUENCE FROM INDIA
Priyanka Bhardwaj & Dr. Uma Shankar Yadav
Page 1-3
| Paper TitleA STUDY OF MATHEMATICAL INFLUENCE FROM INDIAAbstractWith the assault of Islamic invasions and the transformation of colleges and universities into madrasahs, the study of mathematics seems to be on the decline. Moreover, around this time, more and more mathematical literature was being translated into Arabic and Persian. The advent of the Harappan decimal system, mathematics in the Vedic period, Panini and scientific notation, the Indian numeral system, the significance of astronomy, applied mathematics, and philosophy and mathematics are all covered in this essay. KEYWORDS : Numbers, History of Mathematics
| | viewed : | 95 Downloads |
2.
STUDY ON IMPORTANCE OF THE COMPUTER DATA ANALYSIS IN APPLIED MATHEMATICS
Archana Deepak Malpathak & Dr. Rajeev Kumar
Page 5-10
| Paper TitleSTUDY ON IMPORTANCE OF THE COMPUTER DATA ANALYSIS IN APPLIED MATHEMATICSAbstractThe mechanical manufacturing industry has grown to be a foundational sector of the country\'s economy as industrialization has continued to advance. Since so many professional courses must be founded on sound mathematical understanding, mathematics courses play a crucial role in professional instruction. While there is a significant difference between professional teaching and professional teaching from the viewpoint of professional teaching, students do not need to fully engage in the mathematical teaching goals. This research identifies the gaps and weaknesses in the existing approach to teaching mathematics using a questionnaire survey, a review of the literature, and an analysis of the data. Pupils are not passionate about mathematics, they do not really connect with the topic, and professors are unable to pique students\' interest. I\'m hoping that this essay may inspire some fresh approaches to teaching arithmetic. KEYWORDS : Mathematics Teaching, Applied Mathematics, Data Analysis, Teaching mode
| | viewed : | 108 Downloads |
3.
AN EXPLANATION OF EVOLUTIONARY ALGORITHMS AND THEIR USES
Konduru Naveen Kumar Raju & Dr. Suchi Jain
Page 11-23
| Paper TitleAN EXPLANATION OF EVOLUTIONARY ALGORITHMS AND THEIR USESAbstractThis work presents evolutionary techniques for multi-objective optimization. Elitist and non-elitist multiobjective evolutionary algorithms are compared here. Constrained multiobjective evolutionary algorithms are also discussed KEYWORDS : evolutionary algorithms, multi-objective optimization, pareto-optimality, elitism.
| | viewed : | 98 Downloads |
4.
ANALYSIS OF DEEP NEUTRAL NETWORKS IN ACOUSTIC MODELING
Vibhute Pritish Mahendra Dr. Mohammad Iliyas & Dr. Bharat Gupta
Page 24-28
| Paper TitleANALYSIS OF DEEP NEUTRAL NETWORKS IN ACOUSTIC MODELINGAbstractIn this work, we propose a modular combination of two popular applications of neural networks to large-vocabulary continuous speech recognition. First, a deep neural network is trained to extract bottleneck features from frames of mel scale filterbank coefficients. In a similar way as is usually done for GMM/HMM systems, this network is then applied as a nonlinear discriminative feature-space transformation for a hybrid setup where acoustic modeling is performed by a deep belief network Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. KEYWORDS : Acoustic Modeling, Deep Belief Networks.
| | viewed : | 106 Downloads |
5.
AN ANALYSIS OF TRADITIONAL INVERTER AND Z- SOURCE INVERTER
Thokal Prashant Vijay Dr. Kasa Chiranjeevi & Dr. Maram Ashok
Page 29-37
| Paper TitleAN ANALYSIS OF TRADITIONAL INVERTER AND Z- SOURCE INVERTERAbstractA combination of factors including the inevitability of a decline in fossil fuel supplies and the negative impacts on the environment has increased interest in finding renewable energy alternatives. Solar and wind power, two examples of renewable energy, are much superior than fossil fuels. Manufacturing improvements have reduced the cost of materials and improved energy collection, propelling solar photovoltaic (PV) modules to the forefront of the renewable energy industry. As an additional illustration of solar energy use, the electricity produced by PV panels is clean, flexible, and pollution-free. The power conversion step is necessary for the PV source to interact with the existing electrical utilities since it is a low voltage dc source. The high dc voltage required to link the central inverter is achieved by connecting many panels in series and/or parallel in big solar power systems. A significant drop in system output power may occur if even a single PV module experiences poor solar irradiation or low cell temperature due to a shift in ambient circumstances. A traditional grid tie inverter becomes a dynamic reactive power compensator, by regulating the output voltage of the inverter. The ZSI overcomes the restrictions and conceptual boundaries of the VSI (voltage source snverter) and CSI (current source inverter). The ZSI has a separate LC network used to combine the converter network to the source and thus gives unique features as compared to traditional VSI and CSI. KEYWORDS : Solar, wind power, fossil fuel, photovoltaic, VSI, CSI, traditional grid.
| | viewed : | 109 Downloads |
6.
A CRITICAL EXAMINATION OF THE ROLE OF GREEN CHEMISTRY IN ORGANIC SYNTHESIS
Anu Kumari & Dr. Harsh Sharma
Page 38-48
| Paper TitleA CRITICAL EXAMINATION OF THE ROLE OF GREEN CHEMISTRY IN ORGANIC SYNTHESISAbstractGlobal warming and pollution create major issues. As human everyday appliances need had expanded over years, organic chemical-based companies boosted their manufacturing method. It worsens environmental contamination. Hence, green chemistry encouraged chemical companies to be more environmentally friendly. Green chemistry concepts have influenced organic chemistry for 20 years, particularly because many researchers have focused on it. Organic compound synthesis has explored waste avoidance, cleaner solvents, energy efficiency, and renewable feed stocks. This review summarizes green chemistry concepts and their use in organic chemical synthesis. KEYWORDS : environmental sustainability, green chemistry, organic compound, synthesis process.
| | viewed : | 109 Downloads |
7.
PRELIMINARY PHYTOCHEMICAL EVALUATION OF SYZYGIUM CUMINI (L.) SKEELS LEAVES
Prakash Ramrao Kadlag
Page 49-51
| Paper TitlePRELIMINARY PHYTOCHEMICAL EVALUATION OF SYZYGIUM CUMINI (L.) SKEELS LEAVESAbstractJamun, also known as Syzygium cumini (L.) Skeels (Family: Myricaceae), is a popularly used medicinal herb in Ayurveda. Physical-chemical analyses revealed total ash to be 3.1%, acid-insoluble ash to be 0.7%, extractive values to be 10.96% for alcohol and 12.32% for water. Sugar, lipid, glycoside, saponins, phenols, flavonoids, tannins, tri-terpenoids, and steroids were all found in the samples after phytochemical examination. The investigation creates a report on physicochemical parameters that could be helpful for the plant\'s authenticity and identification. KEYWORDS : Jamun leaf, phytochemical, Syzygium cumini, Standardisation.
| | viewed : | 92 Downloads |
8.
A REVIEW OF RESEARCH ON DEEP LEARNING-BASED OBJECT DETECTION
Rasika Sachin Golhar & Dr. Pawan Kumar Pareek
Page 52-56
| Paper TitleA REVIEW OF RESEARCH ON DEEP LEARNING-BASED OBJECT DETECTIONAbstractTarget detection has been a significant research hotspot and an extensively utilized problem in computer vision during the last 20 years. In a given picture, it seeks to rapidly and precisely detect and locate a large number of items according to predetermined categories. The algorithms may be split into two categories based on the model training method: single-stage detection algorithms and two-stage detection algorithms. The typical algorithms for each level are thoroughly presented in this work. Then, numerous typical techniques are examined and contrasted in this area while public and special datasets that are often utilized in target identification are presented. The probable difficulties in target detection are therefore anticipated. KEYWORDS :
| | viewed : | 102 Downloads |
9.
DIABETES PREDICTION USING MACHINE LEARNING ALGORITHMS LIKE SVM, NB AND LGBM
Mrs. M. NARMADHA Mr. K. SESHAGIRI RAO & Mrs. K. PRIYANKA
Page 57-62
| Paper TitleDIABETES PREDICTION USING MACHINE LEARNING ALGORITHMS LIKE SVM, NB AND LGBMAbstractDiabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. In this paper, we use supervised machine-learning algorithms like Support Vector Machine (SVM), Naive Bayes classifier and Light GBM to train on the actual data of 520 diabetic patients and potential diabetic patients aged 16 to 90. Through comparative analysis of classification and recognition accuracy, the performance of support vector machine is the best.
KEYWORDS : Diabetes, Machine, Learning, Prediction, Dataset,Support vector machine.
| | viewed : | 101 Downloads |
10.
AN EXAMINATION OF FUZZY TOPOLOGICAL SPACES
Akash Gadge & Dr. Rahul Dwivedi
Page 63-66
| Paper TitleAN EXAMINATION OF FUZZY TOPOLOGICAL SPACESAbstractIn the world of engineering and technology, the most significant challenges come in the form of doubt and haziness.The use of fuzzy topology may be utilized to successfully find answers to such issues. Researchers investigate fuzzy topological concepts by using the appropriate complement functions in their work. The fuzzy topological ideas are able to be hypothesized with the help of the various complement functions that are found in the fuzzy literature.This article\'s focus will be on investigating some of the fundamental ideas behind fuzzy topological spaces. KEYWORDS : Fuzzy topological space (FTS), fuzzy continuous map, fuzzy semi-compact spaces, fuzzy semi- connectedness, fuzzy weakly-compact spaces.
| | viewed : | 93 Downloads |