Scientific Digest : Journal of Applied Engineering
"Scientific Digest : Journal of Applied Engineering" appears to be a publication focused on research and advancements in the field of applied engineering. Journals like this typically feature articles, studies, and reviews that cover various engineering disciplines, including but not limited to mechanical, civil, electrical, and software engineering. Editor- In-Chief : Dr. AMAR GERA Title proper : SCIENTIFIC DIGEST : JOURNAL OF APPLIED ENGINEERING ISSN : 2348-4802 Subject : Multidisciplinary Publisher : SCIENTIFIC DIGEST : JOURNAL OF APPLIED ENGINEERING Dates of publication : 2013 Frequency : By Monthly Language : English Country : India Medium : Online Indexed by : ROAD, THE KEEPERS Impact Factor (IF): 6.734 Submit Manuscript : joae.editor@gmail.com Title DOI of the Journal : http://doi.org/10.70864/issn.2348-4802
ScientificDigest: Journal of Applied engineering
Data Science Techniques in Pega – Optimizing Business Process Automation
Artificial Intelligence (AI) and Data Science techniques are revolutionizing Business Process Automation (BPA) by enabling intelligent, data-driven decision- making. Pega Systems, a leading low-code business process management platform, integrates AI-driven predictive analytics, process mining, reinforcement learning (RL), and natural language processing (NLP) to optimize workflows, improve efficiency, and enhance customer engagement. This research presents a comprehensive AI-driven BPA framework leveraging machine learning models for workflow prediction, process mining for compliance, RL for dynamic decision optimization, and NLP for automated case management. The proposed approach was evaluated using real-world enterprise workflow datasets, demonstrating significant improvements in workflow compliance (from 78% to 94%), process efficiency (25% increase), and automation effectiveness (60% reduction in manual efforts). The results confirm that AI-integrated BPA in Pega Systems significantly enhances decision automation, operational efficiency, and business agility.
Data Science, Business Process Automation,
Copyright (c) 2025 Scientific Digest : Journal of Applied Engineering
Data Governance and Compliance in Pega Applications: Strategies, Best Practices, and Regulatory Requirements
Ensuring data governance and compliance within enterprise applications is a critical challenge, especially in highly regulated industries. Pega applications provide robust capabilities for automating compliance processes, but traditional rule-based approaches often struggle to keep up with evolving regulations and large-scale data environments. This paper explores how Artificial Intelligence (AI) can enhance data governance in Pega applications by leveraging machine learning for anomaly detection, compliance monitoring, and regulatory adherence. We implement an AI-driven compliance framework and evaluate its performance against traditional rule-based systems. The results demonstrate that AI-based approaches significantly improve anomaly detection accuracy, compliance adherence, and governance efficiency. These findings highlight the potential of AI in optimizing regulatory compliance and reducing manual intervention in Pega environments.
THREE-STEP HYBRID BLOCK METHODS FOR SOLVING FIRST-ORDER LINEAR AND NONLINEAR SYSTEMS
For first-order systems that are either linear or nonlinear, this study derives two three-step hybrid block approaches. Applying the collocation and interpolation approach, with power series as the basis function, allows for the derivation to be carried out. By include a single off-grid point and two off-grid points inside the three-step integration interval, respectively, the first and second three-step hybrid block approaches are generated. To evaluate their accuracy and efficiency, the obtained techniques were used to certain linear and nonlinear first-order systems.Based on the outcomes, it is clear that the three-step hybrid block technique including two off-grid spots outperformed its one- off-grid counterpart. We also found that the two derived approaches outperformed the current methods we compared them against. This was evident from the findings. We dug further into the fundamental features of the obtained approaches. The zero-stability, consistence, convergence, and absolute stability regions are all characteristics that fall under this category.
THREE-SPECIES ECOLOGICAL MODEL: HOST- COMMENSAL-NEUTRAL INTERACTIONS WITH RESOURCE CONSTRAINTS AND KENYAN MACROECONOMIC INSIGHTS
Three hosts—S2, S3, and a single commensal—make up the system. In other words, S1 receives benefits from both hosts without suffering any negative or good effects on its own. Additionally, neither S2 nor S3 are biassed. At this location, all three species have few resources. Three non-linear simultaneous differential equations of first order make up the model equations. This work uses a properly built Liapunov's function to prove that the system is globally stable. In addition, the Runge-Kutta fourth order approach is used to obtain the numerical solutions for the growth rate equations.
THE NATURE AND FUNCTION OF AFFIXATION IN ARABIC AND ENGLISH
The affixation phenomena in Arabic and English is an effort to be explored in this study. This study incorporates all the main components of speech, including nouns, verbs, adjectives, and adverbs. It uses comparative language research to highlight both similarities and contrasts. When it comes to the affixation problem, this comparative research shows how varied languages are. In terms of affixation, namely infixes and suffixes, contrastive analysis reveals substantial variances rather than commonalities. As an added bonus, Arabic seems to be the only language that uses infixes in addition to a large number of suffixes. Learners and speakers may benefit from a better understanding of idiomatic expressions if they are cognizant of all these variants.
THE ART OF INNER WORLDS - EXPLORING NARRATIVE TECHNIQUES IN THE NOVELS OF ANITA DESAI
A lot of credit and magic go to Anita Desai for the narrative approach or tactic she used. The inner lives of her characters are just as important to her as their exterior lives. The mental environment, rather than the physical one, occupies her primary attention and care. Using techniques like stream- of-consciousness, internal monologue, and flashbacks and forth, Anita Desai expertly delves into psychological psychology in her works. If a novelist were to describe their characters' inner lives, they would inevitably touch on the emotional side as well as the intellectual. The emotional side is just as important, if not more so, than the intellectual side, which is often severely underdeveloped. An effort at discussing her books' art and style of narration is undertaken in this article.
FIXED POINT SOLUTIONS FOR NONLINEAR SYSTEMS IN SYMMETRIC METRIC SPACES
Using the CLRg property—which fully assumes the condition of closedness of the range of the involved map and has an advantage over the (E.A) property—this paper aims to prove some common coincidence and common fixed point theorems in the domain of symmetric metric space via faintly compatibility, under various contractive conditions. Consequently, authors are currently focusing on this property to generalise the results found in the literature. The findings here build upon and refine earlier, more well recognised findings in the field of fixed point theory
Suppose that A and B are cancellative abelian semigroups and that R is an integral domain.We demonstrate that this semigroupring R[B] may be reduced to a direct sum of R[A]-submodules of the ring of R[Aquotient] as a R[A]-module. In the case of a finite extension of positive affine semigroup rings, we provide a method for calculating the decomposition.This decomposition allows us to calculate different ring-theoretic features of R[B] for polynomial rings over fields, and we demonstrate how to do so for R[A]. Specifically, we provide a fast method for determining the Castelnuovo-Mumford regularity of homogeneous semigroup rings.In a number of novel contexts, we find evidence that supports the Eisenbud- Goto theory. Our algorithms are part of the MACAULAY2 package MONOMIALALGEBRAS.
COMPARATIVE STUDY OF ARITHMETIC-GEOMETRIC (AG) INDEX AND GEOMETRIC-ARITHMETIC (GA) INDEX FOR DIFFERENT GRAPHS
Topological indices are numerical quantities of a graph that are invariant under graph isomorphism. Various topological indices are categorized based on their degree, distance, and spectrum. In this paper, the comparative study is done between arithmetic- geometric ( AG) index and geometric – arithmetic indices. (GA) index which are degree-based topological
ANALYZING KENYA'S MACROECONOMIC INDICATORS WITH PRINCIPAL COMPONENT ANALYSIS AND HIERARCHICAL REGRESSION
This research set out to examine Kenyan macroeconomic factors using a hierarchical regression model and Principal Component Analysis (PCA). A combination of descriptive and correlational research methods was used in the study. From 1970 to 2019, data for the 18 macroeconomic indicators were culled from the Kenya National Bureau of Statistics and the World Bank.For all the data analysis, the R programme was used.In order to lower the data's dimensionality, we used Principal Component Analysis, which reduced the original data set matrix to Eigenvectors and Eigenvalues. To find out whether the extracted components were excellent at forecasting economic development, we fitted them to a hierarchical regression model and used R2 as our measure of success. The study's findings showed that the first component was strongly connected with fifteen original variables and explained 73.605% of the total variance. As an added bonus, the two variables exhibited a larger positive loading into the second main component, which explained around 10.03% of the overall Variance. The third component, which had a strong correlation with only one of the initial factors, explained about 6.22 percent of the total variation. A p-value of 0.0001<5% indicated that the models were significant, while the first, second, and third models had F statistics of 2385.689, 1208.99, and 920.737, respectively. The third model was deemed the most effective predictor due to its 17.296 mean square error. The reliability of Principal Component 1 in describing economic growth was higher than that of other models since it had the largest Variance explained and the lowest p-value. As a result, we may anticipate economic development in Kenya based on the following macroeconomic variables: monetary economy, trade and openness to government operations, consumption, and investment
MITIGATING DDOS ATTACK IN IOT NETWORK ENVIRONMENT
Nowadays, there are lots of Internet of Things (IoT) devices are used in Industry, Home appliances, Automobile industry and many more places. Issues regarding security of the IoT are the primary reason why it fails to attract more people. Each day, beside the new technology comes a millions of vulnerabilities waiting to be exploited. IoT is that the latest trend and like all technology, it's open for exploitation. In IoT environment, Distributed Denial of service attack (DDoS) could be a major issue, because of the limited computing and power resources of standard IoT devices are prioritized in implementing functionality instead of security features. DDoS attack is the most common attack which is used to bring down the whole network without having any loophole in the network security. The main purpose of this work is to mitigate DDoS attacks against the IoT using honeypot model in Raspberry Pi. Honeypots are the network setup with intentional loopholes. The purpose of honeypot is to invite attackers, so the activities and methods used to attack can be studied and it can also help to increase network security. Conclusion came out after reviewing some research papers that there are lots of DDoS attack mitigation techniques are proposed by many researchers, but very few are proposed for IoT environment. Primary focus of this dissertation work is to proposed mitigation techniques of DDoS attack on IoT device using honeypot and IDS with low cost and high performance resources.
Internet of Things, Denial of Service Attack, Distributed Denial of Service Attack, IoT security, Security Breach
This study aims to develop an Inventory Management Systems (IMS) that can provide better control and handling of product stock, customer order, customer service and order delivery that relates to company inventory information. The target user is the owner and employee in Small and Medium Enterprise (SME) retail store that stills manages inventory manually in Malaysia. IMS helps retail store to track down the next arrival of product stocks and record customer order for reservation for the product in the store inventory. In this study, the developer used PHP for backend system development and HTML, CSS, JavaScript for frontend system development. This study also applies Rapid Application Development (RAD) software methodology that emphasize on iterative development process. Even though the inventory management system has been fully developed by the developer, there are still limitations found and future enhancement that can be made towards the system.
CSS, HTML, Inventory Management Systems (IMS), JavaScript, PHP, Rapid Application Development (RAD), Small and Medium Enterprise (SME).
Detection of cyber-attacks and network attacks using Machine Learning
The Internet and computer networks have become an important part of organizations and everyday life. New threats and challenges have emerged to wireless communication systems especially in cyber security and network attacks. The network traffic must be monitored and analysed to detect malicious activities and attacks. Recently, machine learning techniques have been applied toward the detection of network attacks. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates and cheaper computing and transmission costs. Our key goal is detection of cyber security and network attacks such as IDS, phishing and XSS, SQL injection, respectively. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack.
Cyber-crime; Machine Learning algorithms; Phishing attack; Network Intrusion; Cross-Site Scripting (XSS); SQL Injection
Social Media and Misleading Information in a Democracy: A Mechanism Design Approach
In this paper, we present a resource allocation mechanism for the problem of incentivizing filtering among a finitenumberofstrategicsocialmediaplatforms.Weconsiderthe presenceofastrategicgovernmentandprivateknowledgeofhow misinformation affects the users of the social media platforms. Ourproposedmechanismincentivizessocialmediaplatforms to filter misleading information efficiently, and thus indirectly prevents the spread of fake news. In particular, we design an economically inspired mechanism that strongly implements all generalized Nash equilibria for efficient filtering of misleading information in the induced game. We show that our mechanism isindividuallyrational,budgetbalanced,whileithasatleast one equilibrium. Finally, we show that for quasi-concave utilities and constraints, our mechanism admits a generalized Nash equilibrium and implements a Paretoefficient solution.
Social media, fake news, mechanism design, Nash-implementation
MISSING CHILD IDENTIFICATION SYSTEM USING DEEP LEARNING AND MULTI CLASS SUM
nIndia,reportsofmissingchildrenamounttoinfinityeachyear.Alargenumberofthechildrenin casesofmissingchildrenremainunaccountedfor.Thisstudypresentsanoveluseofdeeplearningtechnology based on face recognition to identify the youngster who has been reported missing from a big database of photos. People can post comments and landmarks along with photos of dubious-looking kids on a public platform. The image will instantly be compared to the registered photos in the repository belonging to the missing child. After the supplied child photo has been categorized, the image that most closely matches the databaseofmissingchildrenwillbeselected.Adeeplearningmodelistrainedtocorrectlyidentifythemissing child from the missing child photograph using the publicly available facial image.
CNN, VGG-facial, facial recognition, deep learning, multiclass SVM, missing child identification.
Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques
Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completionratearemajorconcerns.Inthispaper,theearlyidentificationofstudentswho areatriskofwithdrewandfailure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The resultrevealsthat allclassifiersgaingood accuracyacrossboth models, the highestperformance yield by GBM withthevalueof0.894,0.952 forfirst,second modelrespectively,whileRFyieldthevalueof0.866,inat-riskstudent framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at- risk students.
MachineLearning;MassiveOpenOnlineCourses;ReceiverOperatorCharacteristics; Area Under Curve.
RESPIRATORY ANALYSIS DETECTION OF VARIOUS LUNG INFECTIONS USING COUGH SIGNAL
Every year, a considerable number of individuals, regardless of age, pass away from chronic lung illnesses. Lung sound analysis is a vital demonstrative technique for precisely diagnosing pulmonary disorders. Lung disorders were formerly diagnosed manually, however this approach was inaccurate for a number of reasons, such as limited perceptibility and variations in contrast across physicians' eyes for various sounds. With the increased accuracy of results from modern research, patients with various lung ailments may now get better care. Among these issues include pneumonia, TB, emphysema, bronchitis, and asthma. Negative symptoms include rhonchi, fatigue, wheezing, and chronic hacking. In this effort, we are predicting a range of illnesses, such as bronchiectasis, pneumonia, and asthma, using respiratory sound datasets. To do this assignment, we first extracted the components from each sound dataset—the respiratory sound dataset and the illness conclusion dataset—and then built a convolution brain organization (CNN) calculation model. Once the model has been created, we may include any new test data and predict infection from it.
FAKE NEWS DETECTION USING DS APPLICATIONS
The problem of fake news is getting serious day by day and it has very detrimental impacts in different spheres like that of politics. Due to the availability of social media, it becomes very easy to spread fake news and it becomes difficult to identify the culprit. As the culprit of such actions is not identified, the problem still persists. Hence, there is aneed to identify the source or origin of such fake news to take the necessary action. There are several methods to detect fake news but the common people rarely have time to use these detectiontechniquesandtheyeasilyfallpreytosuchpiecesof information. In this paper, we review the different methods that are applied to detect fake news and address it’s limitations. We propose the flow of a new blockchain based system that can be used by the news agencies to ensure the validity of the news published. With the help of such asystem, the news will be entirely traceable and trackable to everysinglepointintheagencyandhence,itbecomeseasy to find the source and identify the culprit in case of any misinformation.
Blockchain, Fake News, Fake News Detection using blockchain, Blockchain to curb fake news
AUNTHENTICATION OF PRODUCT &COUNTERFEITS ELIMINATION USING BLOCK CHAIN
Blockchain technologies have gained interest over the last years. While the most explored use case is financial transactions, it has the capability to agitate other markets. Blockchain remove the need for trusted intermediaries, canfacilitate faster transactions and add more transparency. This paper explores the possibility to deflate counterfeit using blockchaintechnology.Thispaperprovidesanoverviewofdifferentsolutionsintheanti-counterfeitarea,different blockchaintechnologiesandwhatcharacteristicsmakeblockchainespeciallyinterestingfortheusecase.Wehave developed three different concepts and the expansion of an existing system concept, is pursued further. It is shown, that reducingcounterfeitscannot beachievedbyusingtechnological meansonly.Increasingawareness,fightingcounterfeiters onalegallevel,agoodalertsystem,andhavingtamper-proofpackagingareallimportantaspects.Thesefactors combined with blockchain technology can lead to an efficient and comprehensive approach to reduce counterfeiting Keywords – Authentication, Blockchain, Encryption
CROP YIELD PREDICTION USING MACHINE LEARNING
Agriculture is one of the major and the least paid occupation in India. Machine learning can bring a boom in the agriculture field by changing the income scenario through growing the optimum crop. This paper focuses on predicting the yield of the crop by applying various machine learning techniques. The outcome of these techniques is compared on the basis of mean absolute error. The prediction made by machine learning algorithms will help the farmers to decide which crop to grow to get the maximum yield by considering factors like temperature, rainfall, area, etc. The classifier models used here include Logistic Regression, Naive Bayes and Random Forest, out of which the Random Forest provides maximum accuracy. The prediction made by machine learning algorithms will help the farmers to come to a decision which crop to grow to induce the most yield by considering factors like temperature, rainfall, area, etc. This bridges the gap between technology and agriculture sector.
CREDIT CARD FRAUD DETECTION USING STATE-OF-THE-ART MACHINE LEARNING AND DEEP LEARNING ALGORITHMS
redit card fraud continues to pose a significant threat to financial institutions and consumers worldwide. In recent years, the proliferation of advanced technology has enabled fraudsters to develop increasingly sophisticated methods for perpetrating fraudulent transactions. To combat this ever-evolving challenge, this study explores the application of state-of-the-art machine learning and deep learning algorithms for credit card fraud detection. This research leverages a comprehensive dataset containing both legitimate and fraudulent credit card transactions, allowing for the evaluation of various detection methods. We employ a diverse set of machine learning and deep learning models, including Random Forest, Support Vector Machine, Gradient Boosting, and Convolutional Neural Networks (CNNs), among others, to assess their performance in identifying fraudulent activities. The results of our experiments demonstrate the efficacy of deep learning techniques, particularly CNNs, in achieving higher accuracy and improved fraud detection rates when compared to traditional machine learning algorithms.
CNN, DL,ML, Fraud detection, high efficiency
A MULTI-STREAM FEATURE FUSION APPROACH FOR TRAFFIC PREDICTION
ighway traffic accidents remain the biggest cause of mortality notwithstanding an increase in web traffic awareness. Road accidents pose a serious hazard to people's lives and property in emerging nations. Traffic accidents are caused by a variety of factors, some of which are more important than others in determining how serious an accident is. Data extraction methods can help with the prediction of key aspects of collapse intensity. In this study, utilising Random Forest, it was discovered that a number of characteristics have a strong correlation with the seriousness of highway crashes. Range, temperature, wind chill, humidity, exposure, and wind orientations are the main factors influencing surprise severity. To forecast the severity of traffic accidents, this study blends RFCNN, or Random Forest and Convolutional Semantic Network, with existing deep learning and artificial intelligence models. Comparing the effectiveness of the proposed strategy to a variety of fundamental learner classifiers is necessary. The crash statistics for the United States from February 2016 to June 2020 are among the data considered in the analysis
OSN, Spam, fake account, URL, twitter, social media.
WATERNET A NETWORK FOR MONITORING AND ASSESSING WATER QUALITY FOR DRINKING AND IRRIGATION PURPOSES
Constructing Sustainable Smart Water Supply systems are facing serious challenges all around the world with the fast expansion of modern cities. Water quality is influencing our life ubiquitously and prioritizing all the urban management. Traditional urban water quality control mostly focused on routine tests of quality indicators, which include physical, chemical and biological groups. However, the inevitable delay for biological indicators has increased the health risk and leads to accidents such as massive infections in many big cities. In this paper, we first analyze the problem, technical challenges, and research questions. Then we provide a possible solution by building a risk analysis framework for the urban water supply system. It takes indicator data we collected from industrial processes to perceive water quality changes, and further for risk detection. In order to provide explainable results, we propose an Adaptive Frequency Analysis (AdpFA) method to resolve the data using indicators’ frequency domain information for their inner relationships and individual prediction. We also investigate the scalability properties of this method from indicator, geography and time domains.
Vehicle Detection Based on Semantic-Context Enhancement for High-Resolution SAR Images in Complex Background
For ITSs, speed and accuracy in identity verification and vehicle ordering are crucial. However, it is difficult to notice and recognise vehicle types rapidly and also accurately due to the close proximity of vehicles on the road and the jarring nature of images or videos that include automobile images. For this task, we recommend using YOLOv4 AF, a service built on top of an improved variant of the original YOLOv4 concept. To reduce channel and geographical variability of picture occlusion, the suggested layout is factored in. The Feature Pyramid Network (FPN) component of the Training Aggregation Network (PAN) has been modified in YOLOv4 to down-inspect the trustworthy highlights. This paves the way for better in-design item ID and characterization implementation, as well as richer information about the 3D locations of individual items. With enhancements of 83.45% and also 0.816 on the Thing Car instructional collection and also 77.08% and also 0.808 on the UA-DETRAC informative collection, specifically, the suggested YOLOv4 AF design surpasses the initial YOLOv4 and also 2 various other cutting-edge versions, Faster R-CNN and also EfficientDet.
DRUG RECOMMENDATION SYSTEM BASED ON SENTIMENT ANALYSIS OF DRUG REVIEWS USING MACHINE LEARNING
Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. This Application intends to present a drug recommender system that can drastically reduce specialists heap. In this project, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes, which can help recommend the top drug for a given disease by different classification algorithms. The results show that MLP classifier outperforms all other models with high accuracy.
AN IMPLEMENTATION OF BLOCKCHAIN TECHNOLOGY IN FORENSIC EVIDENCE MANAGEMENT
The importance of crime forensic data in the criminal justice system is paramount. However, traditional methods of storing this sensitive information have been plagued by issues such as the risk of tampering,humanerror,andcyber- attacks. Thispaperproposesagroundbreaking solutionthat leverages Block chain technology to create a tamper-sproof, decentralized repository for crime forensic data. Unlike existing techniques, which often rely on manual documentation or centralized servers, the proposed block chain-based system offers inherent data integrity, auditability, and security features. By shifting to a decentralized model, we not only reduce the risk of unauthorized alterations but also facilitate a more transparent and trustworthy environment for all stakeholders involved in the judicial process. The paper delves into the architectural considerations, security features, and potential challenges associated with implementing this blockchain-based system. Preliminary results indicate that this approach could significantly enhance the credibility and reliability of crime forensic data, thereby contributing to a more just and effective criminal justice system.
Blockchain,Forensicevidence,Mana gement,Implementation
Integrated Land Management Information System
Many endogenous factors have complex joint and causal interactions, making it difficult to predict travel behaviour. It is also widely agreed upon that there is a connection between the method of transportation chosen for the commute and whether or not to include interim activities into a work trip. Yet, the specifics of the connections between the two are unclear. This study proposes a mathematical model to inquire into the sequence of trip chaining and travel mode selection, allowing for a more in-depth investigation of these topics. This research employs the co-evolutionary technique to capture the interaction between transport mode choice and trip chaining using household travel survey data from Beijing, China. Two MNL models, one for mode selection and another for trip-chaining, are integrated with a co-evolutionary strategy. Commuters' judgements on which form of transportation to use and how to sequence their trips differed, as shown by the data. Yet, the most prominent pattern is that trip chaining determines which mode is selected.
CURRENCY RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM
Indian currency recognition system using image processing technique used to accurately identify bank notes. The system focuses on recognizing paper currency by analysing images or photographs of the notes, specifically targeting the Indian rupee. It operates by first determining the currency’s country of origin through predefines areas of interest, followed by extracting the denomination based on key characteristics such as size, color, and printed text. The methodology is designed to handle the complexities of distinguishing between various methods, the system is capable of quickly and accurately identifying currency notes, thereby facilitating smoother transactions and reducing the potential for errors in currency recognition. This approach not only streamlines the counting of mixed denomination notes but also contributes to the broader application of automated currency recognition system in banking and financial services.
TO PREDICT PLANT GROWTH AND YIELD IN GREEN ENVIRONMENT USING DEEP LEARNING
Greenhouse growers and farmers in general rely heavily on accurate predictions of plant growth and harvest. Improvements in environmental management, increased output, improved supply-and-demand matching, and reduced costs may all result from the development of models that can properly simulate growth and yield. Recent advances in ML, and especially Deep Learning (DL), may give robust new analytical tools. Researchers seek to employ ML and DL methods to estimate production and plant growth variance in two greenhouse settings: tomato yield forecasting and Ficus benjamina stem growth. In the prediction formulations, we use a novel deep recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) neuron model. In order to simulate the desired growth parameters, the RNN design takes into account both the historical values for yield, growth, and stem diameter, as well as the microclimate circumstances. We propose a research that uses the mean square error criteria to compare the results of several ML approaches, such as support vector regression and random forest regression. Positive findings are given based on information collected from two greenhouses in Belgium and the United Kingdom as part of the EU
Prediction, deep learning, recurrent LSTM neural networks,
growth, yield, tomato, ficus, stem diameter.
LED DOT Matrix Text Recognition method in Natural Scene
Light-emittingdiodestextdot-matrixtext(LEDtext)isbeingwidelyusedfordisplayinginformationandannouncements. LED display for modernization of society and catch on for its versatile application with many benefits. Existing paper used k-nearest neighbor(k-NN) approach,lowcomputationcomplexitymethodforpatternrecognition, isusedtorecognitioncharactercomponentas any class of character and canny edge was used to detect character pixels when appear in led display area from scene images. The drawback of existing system is that it cannot handle text line with non-uniform color and containing less than 3 characters. It also cannot detect continuous LED text. Our proposed system will utilize the probabilistic neural network (PNN) classification to add the robustclassificationforthehigherleveloftheadaptiveness. Principalcomponentanalysis(PCA)isastatisticalprocedurethatusesan orthogonal transformation toconvert aset of observations of possibly correlated variables into aset of values of linearlyuncorrelated variables called principal components. Our proposed system will achieve better detection and recognition rate than existing system
Neuralnetwork,pca,images,greyscale, images.