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International Conference on Advanced Trends in Multidisciplinary Research (IC-ATMR)

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Articles

Efficient usage of power, water and crop production in agriculture by using AI

Dr.R.Aruna, N. Hiranmayee, Dr. Rajeev Sharma, Singamaneni Krishnapriya, Dr.Vivek Pimplapure, Dr.KGurnadha Gupta,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Using synthetic intelligence (AI) in agriculture has extensive potential for fixing urgent problems and enhancing resource management. By analyzing actual-time statistics from sensors and smart devices, identifying strength-in depth obligations, and recommending power conservation measures, AI-based totally structures can optimize strength use. Artificial intelligence (AI) algorithms may be used to review and alter power intake patterns, ensuing in less power being wasted, less expensive strolling prices, and a smaller carbon impact. This cognizance on power performance is especially vital in view of the rising price of power and the negative environmental consequences of agriculture. Water scarcity is every other pressing problem in agriculture, which often leads to excessive water use while utilizing traditional irrigation strategies. By analyzing records from many sources, which include weather forecasts, soil moisture sensors, and crop growth models, AI can play a big function in intelligent water management. AI algorithms can be used to create the best irrigation schedules, locate irrigation machine leaks or inefficiencies, and adopt precision watering strategies. These clever water management techniques no longer handiest minimize water use but additionally ease agricultural water stress and shield priceless water sources, promoting agricultural sustainability.

THE CRITICAL ANALYSIS ON THE IMPACT OF ARTIFICIAL INTELLIGENCE ON STRATEGIC FINANCIAL MANAGEMENT USING REGRESSION ANALYSIS

Dr. Bhadrappa Haralayya, Dr.Mamta Mallikarjun, P Sripalreddy,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Knowledge of the role that AI plays in strategic management is critically important is critical, given that AI enables the automation of a substantial number of management-related responsibilities and is progressively being incorporated into an extensive array of strategic obligations. Conversely, the body of research concerning the relationship between artificial intelligence and strategic management is characterized by a lack of structure and coherence, resulting from the integration of perspectives from diverse academic fields. The vast amount of study that has been undertaken on the subject since its first publication in 1979 is organized and compiled on this webpage, making a contribution to academia. In addition, the ideas discussed in the previous parts are integrated and harmonized within its comprehensive framework. The framework creates the groundwork for the field by classifying 58 relevant articles into two research scopes: outcome-oriented research, which looks at the effects of AI on individuals and organizations, and condition-oriented research, which investigates the requirements for using AI in strategic management. This article proposes future study directions based on the established framework that investigate the measurable impacts of AI's interaction with strategic management. Given the immense power of AI to transform the business and the need of accurately predicting its consequences, the following suggestions are made. Key terms include AI, strategic financial management, and regression analysis.

Deep learning-based detection of ultrasonic signal and its recognition using wavelet fingerprints

Ajay Reddy Yeruva, Dr Sridhar Manda, Malleswara Rao , Banoth Veeru, Dr. Arun Kumar Arigala, Dr. L Malleswara Rao,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

The fundamental instrument is a stream- lined time-frequency (scale) projection known as a dynamic wavelet fingerprint. Utilizing the wavelet transform’s matching filter and adaptive time-frequency analysis properties, the dy-namic wavelet fingerprint methodology is a linked detection and recognition method. Unlike conventional value-based techniques, the dynamic wavelet fingerprint-based technology is based on patterns or knowledge. This research presents an novel detection and characterization technique for ultra-Sonic’s s signal’s

FAKE PRODUCT DETECTION USING BLOCKCHAIN

Dr. M. Sunil Kumar, M. Sowmya vani, N.Pavithra, R. Chandana, N.Jhansi,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Products that are counterfeit pose a serious threat to both consumers and businesses. Our suggested method makes use of blockchain technology to establish an open and trustworthy platform for tracking phone goods all the way through the supply chain. Each product is assigned a unique digital ID stored on the blockchain, along with origin and production details. Stakeholders can scan the ID to verify product integrity. IoT and machine learning enhance identification by providing real-time data and analyzing patterns. Suspected counterfeit cases are flagged for investigation. This blockchain-based system protects brand reputation, revenue, and enables informed purchases. Blockchain apos; s immutability prevents tampering, reducing fake goods prevalence.

Crop Prediction Based on Characteristics of the Agricultural Environment Using Machine Learning Classifiers

Dr. M. Sunil Kumar, Ms. M. Sowmya Vani, Ms. P. Neeraja, S. Lavanya, R. Roja, V.Gopi Krishna,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Developing is a making field of examination. Expecting crops is especially critical in agribusiness since it by and large depends upon the temperature, tenacity, and precipitation on the soil. Farmers used to have the choice to pick the respect create, keep an eye out for its development, and pick when it might be gathered. Regardless, the developing neighborhood finds it trying to do as such on account of the fast changes in the environment. As a result, imitated insight frameworks have recently taken control of the assumption task, and our study has used many of them to improve agricultural productivity. Using practical part assurance strategies to pre-process rough data into a successfully measurable dataset that handles simulated intelligence is critical if one wants to ensure that a given computer-based intelligence (ML) model works with a high level of accuracy. Just data incorporates that expect a colossal part in concluding the model apos; s last outcome ought to be used to diminish redundancies and work on the precision of the model. Accordingly, ideal part assurance arises to ensure that the model integrates only the most pertinent components. Our model will be unreasonably jumbled expecting that every part from the unrefined data is solidified without being checked as far as concerns them in the exhibiting framework. In addition, the precision of the model apos; s outcome will be impacted by additional features that commit to the ML model apos; s presence unpredictability. The disclosures show that stood out from the continuous gathering technique, a social occasion strategy gives better estimate accuracy.

AI-Powered Hybrid Models for Malicious URL Identification: Enhancing Cyber security through Advanced Threat Detection

Dr. M. Sunil Kumar, MS.M.Sowmya Vani, G.Jahnavi, G.Thejasri, J.Joshika,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

The difficulty of identifying harmful URLs on the Internet is described in the abstract of the study "Identifying Malicious URLs Using Hybrid Models in Machine Learning," which also suggests a hybrid machine learning technique as a possible solution. The writers focus on the Identifying malicious URLs can be difficult since cyber threats are dynamic and continually changing. They next go over their strategy for detecting fraudulent URLs with high accuracy, which includes several machine learning methods, including decision trees, support vector machines, and artificial neural networks The authors demonstrate that their hybrid approach beats individual learning algorithms by evaluating the performance of their model utilising a dataset of actual dangerous URLs. In general, the study offers a viable answer to the crucial issue of recognising rogue URLs.This research introduces innovative AI-powered hybrid models designed for the identification of malicious URLs, contributing to enhanced cybersecurity through advanced threat detection. Leveraging a sophisticated blend of machine learning algorithms and heuristic analysis, the proposed models demonstrate superior accuracy in identifying and categorizing potentially harmful URLs. The system intelligently analyzes diverse features, including URL structure, content, and historical data, to discern malicious intent. Through comprehensive evaluation and validation, our approach showcases robust performance across varying types of cyber threats. This research contributes to the development of proactive defense mechanisms, reinforcing cybersecurity infrastructure against evolving online threats

AI-Driven Forecasting Mechanism for Cardiovascular Diseases: A Hybrid Approach Using MLP and K-NN Models

Dr. Yadaiah Balagoni, Pokuri Venkataradhakrishnamurty, Addala Hemantha Kumar, G.Sudha Gowd, Chinnala Balakrishna, Dr.K.Gurnadha Gupta,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Cardiovascular disease (CVD) results in malfunctioning of the heart and also the related blood vessels and regularly primes to death or corporal paralysis. Initial and automated discovery of CVD is crucial for saving many of the common humanoid lives. While several Investigations have been carried out to achieve this goal, and there is still room for development in terms of performance and reliability. This new work provides one more step in this area, since it utilizes two reliable ML algorithms, "multi-layer perceptron (MLP)" and "K-nearest neighbor (K-NN)"to detect CVD using publicly available data from the University of Engineering California Irvine source. The models apos; concerts are enhanced optimally and by eradicating the possible outliers and common existing attributes with values that are treated as null. They performed general Experimental and effective findings show that the MLP model outperforms the K-NN model in terms of reporting accuracy (82.47%) and area below the curve (86.41%). As a result, the proposed MLP model is recommended for automated CVD identification. Furthermore, this approach may be used to identify new illnesses, and the suggested model apos; s routine can be tested against other standard datasets.

Online Transaction fraud detection and prevention using HMM and Behavior Analysis

Mr.Abjijeet More, Dnyaneshwari Khane, Mansi Nagane, TanujaBhoir,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

The objective of this research paper is to identify and prevent fraudulent bank transactions. With the rapid expansion of internet technology, credit card payments have become a common method of payment for both offline and online purchases, as the use of online transactions, such as NEFT and RTGS, has increased. Despite this, there has been an increase in online transaction deception. Criminals have inflicted enormous losses on banks by capitalizing on the widespread use of network transactions to perpetrate crimes. At the moment, fraudulent transactions remain undetected until the fraud detection system verifies their completion. By applying the Hidden Markov Model (HMM) to this issue, fraudulent activities can be promptly averted through the implementation of a transaction verification code that is emailed to the user. An area l-time payment gateway mechanism has been incorporated into the system in order to optimize its performance. Furthermore, behavior analysis will be implemented to gain insights into the spending habits of the user. This HMM and behavior analysis combination will facilitate sophisticated fraud analysis with a low false alarm ratio.  Transaction, Hidden Markov Model (HMM), Fraud Detection System (FDS), Card Holder, Behavior Analysis (BA).

AI-Enhanced Implementation of Period-Based Costing (DBC) in Iraqi Manufacturing Companies: A Case Study Analysis

Hatem Karim Kadhim, Ali Quddous Haraj,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

The paper aims to demonstrate the role of the period-based costing (DBC) technique in determining the cost of the product and the extent of its reflection on the decisions of the economic unit. In the decision-making process, it is also necessary to make a comparison between (DBC) technology, (ABC technology) and (TDABC) technology in how to allocate and deal with costs and determine the utilized energy. The time-based costing technique has an influential role in reducing costs compared to the techniques indicated by the research and determining the exploited and unexploited energy of the research sample. The findings suggest that Direct production is the bulk of production costs, which helps it to maintain the competitive advantage to face the challenges of intense competition. In terms of cost reduction, DBC technology has contributed to reducing the indirect industrial costs of the products of the research sample compared to the two technologies mentioned above.

The Role of Cloud Computing in Studying the Effect of Annealing Temperatures on the Structural and Magnetic Properties of Cobalt Ferrite Nanomaterials

Khushboo Kumari, Raj Kumar Gupta, Harendra Kumar Satyapal,

Year: 2023 | Conference Paper | Publisher: Scitech Conference Xplorer

Abstract:

Nanomaterials made of cobalt ferrite (CoFe2O4) have drawn a lot of interest because of its potential uses in a number of industries, including energy storage, catalysis, and spintronics. By adjusting the annealing temperature, CoFe2O4 nanoparticles' structural and magnetic characteristics can be customized. To investigate how annealing temperatures, affect the structural and magnetic characteristics of CoFe2O4 nanoparticles, however, can be quite computationally intensive. This problem can be solved via cloud computing. High-performance computing resources are accessible on demand thanks to cloud computing. This eliminates the need for researchers to invest in their own high-performance computing infrastructure in order to explore the impact of annealing temperatures on the structural and magnetic properties of CoFe2O4 nanoparticles. We provide a cloud-based framework for investigating the impact of annealing temperatures in this paper. In this article, we provide a cloud-based platform for researching how CoFe2O4 nanoparticles' structural and magnetic characteristics are affected by annealing temperatures. The platform analyzes the data produced by the simulations using a range of machine learning methods.


International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

International Conference on Sustainable Energy and Materials Engineering (ICSEME)

International Conference on Biomedical Robotics and Computational Imaging (ICBRCI)

International Conference on Smart Cities and Civil Infrastructure (ICSCCI)

International Conference on Aerospace Technologies and Data Science (ICATDS)

International Conference on Renewable Resources and Chemical Engineering (ICRRCE)

International Conference on Cyber-Physical Systems and Electrical Engineering (ICCPSE)

International Conference on Robotics in Manufacturing and Environmental Engineering (ICRMEE)

International Conference on Advanced Materials and Mechanical Engineering (ICAMME)

International Conference on Nanotechnology for Electrical Systems (ICNES)

International Conference on Geotechnical Innovations and Computer-Aided Design (ICGICAD)

International Conference on Water Resources and Environmental Engineering (ICWREE)

International Conference on Intelligent Transportation Systems and Structural Engineering (ICITSE)

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