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Ideal co-secure domination in graphs
A set S ? V of a graph G = (V, E) is a co-secure dominating set if for every u ? S, there exists v ? V \ S such that uv ? E and (S \ {u}) ? {v} is a dominating set of G. The minimum cardinality of a co-secure dominating set of G is the co-secure domination number and is denoted by ?cs(G). In this paper we initiate the evaluation of a domination parameter known as the ideal co-secure domination and is defined as follows: A set D ? V is an ideal co-secure dominating set of a graph G = (V, E) if for every u ? D and for every v ? V \ D such that uv ? E, (D \ {u}) ? {v} is a dominating set of G. The minimum cardinality of an ideal co-secure dominating set of G is the ideal co-secure domination number and is denoted by ?ics(G). We look to determine the ideal co-secure domination number of some families of standard graphs and obtain sharp bounds. We also provide the conditions necessary for the trees to have ideal co-secure domination number equal to n - 2. 2020 Author(s). -
Identification of Consumer Buying Patterns using KNN in E-Commerce Applications
In recent days, with the advancement of technologies, people use electronic medium to carry out their businesses. E-commerce is a process of allowing people to buy and sell products online using electronic medium. E-commerce has a wide range of customer base as well. The data generated through transaction helps the enterprises to develop the marketing strategy. The growth of this e-commerce application depends on several factors. Some of the factors are follows 1) Customer demand, 2) Analyzing buying pattern of the users, 3) Customer retention, 4) dynamic pricing etc. It is very difficult to analyze the buying pattern of customers as there is a wide range of customer base in the online platform. To overcome this problem, this research study discusses about the challenges and issues in e-commerce applications, also identifies and analyses the buying patterns of customer using various machine learning techniques. From the implementation it is identified that, KNN algorithm performed well while comparing it with various other machine learning algorithms. Performances of these algorithms have been analyzed using various matrices. For analyzing, the model is tested using e-commerce dataset (Amazon dataset downloaded from Kaggle.com). From the analysis it found that KNN algorithm computes and predicts better compared to other machine learning algorithms either Nae Bayes, or Random Forest, or Logistic Regression etc. 2023 IEEE. -
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE. -
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE. -
Identification of Phishing URLs Using Machine Learning Models
In this study, we provide a machine learning-based method for identifying phishing URLs. Sixteen features, including Have IP, Have At, URL Length, URL Depth, Non-standard double slash, HTTPS domain, Shortened URL, Hyphen Count, DNS Record, Domain age, Domain active, iFrame, Mouse Over, Right click, Web Forwards, and Label, were extracted from the 600,000 URLs we gathered as a dataset of legitimate and phishing URLs. We then used this dataset to train a variety of machine learning models. These included standalone models such Naive Bayes, Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). We also used ensemble models likeHard Voting, XGBoost, Random Forests, and AdaBoost. Finally, we used deep learning models such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN).On evaluation of performance metrics like accuracy, precision, recall, train time and prediction time it was found that XGBoost provides the best performance across all categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Identification of Predominant Genes that Causes Autism Using MLP
Autism or autism spectrum disorder (ASD) is a developmental disorder comprising a group of psychiatric conditions originating in childhood that involve serious impairment in different areas. This paper aims to detect the principal genes which cause autism. Those genes are identified using a multi-layer perceptron network with sigmoid as an activation function. The multi-layer perceptron model selected sixteen genes through different feature selection techniques and also identified a combination of genes that caused the disease. From the background study, it is observed that CAPS2 and ANKUB1 are the major disease-causing genes but the accuracy of the model is less. The selected 16 genes along with CAPS2 and ANKUB1 produce more accuracy than the existing model which proved 95% prediction rate. The analysis of the proposed model shows that the combination of the predicted genes along with CAPS2 and ANKUB1 will help to identify autism at an early stage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review
This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination can not only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The Survey is hybrid with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction based on texture extraction, color extraction, and shape extraction. The Electrochemical Society -
Identification of Student Programming Patterns through Clickstream Data
In present educational era, teaching programming to the undergraduates is challenging. For an instructor, focusing on each of the aspect of programming like coding language, logical reasoning, debugging errors, troubleshooting code and problem solving is very daunting task. So, educational researchers are identifying ways to easily identify the student's struggles during programming so that timely assistance can be provided. Using programming platforms or software, a lot of programming data is generated in the form of activity logs or clickstream data. Using machine learning along with data analytics over this programming data can reveal programming patterns of students that may help in early interventions. This study focusses on identifying programming patterns of the students through clustering and groups the students into three major categories namely low performers, strugglers, and high scorers. Further, relevant features like test case success, code compile success and failure, finish test etc. that majorly contribute towards the student programming scores are identified through regression analysis. Through this research, educators can early categorize the students based on their programming patterns and provide timely intervention when necessary, ensuring that no student gets left behind in the fast-paced world of programming education. 2024 IEEE. -
Identification of the Functional Limitation of Marine Loading or Unloading Arm; A Case Study
Marine loading or unloading arms are used to transfer product from tanker vessels that often carries products like petroleum or chemicals from or to the tankers. Cochin Port has dedicated Tanker jetties for handling petroleum with Marine Loading Arms installed for safe handling of cargo. However, my studies in Cochin Port Trust have shown that it has a potential threat to tackle while it is taken for the maintenance process. The case study aids in understanding of the working of marine unloading arm installed in the port and to identify the functional or safety limitations of the existing model installed. This case study also proves that a small change in the design can bring about a big change in the safety of the people working with the equipment. The identified parameters have been studied for providing the necessary alterations of the design which could be implemented on the upcoming project of constructing the marine unloading arm in Cochin Port Trust. To support faster and safety loading/unloading requirement these hydraulically operated marine loading arms are fitted with emergency release couplings and emergency release system. Marine Loading Arms are operated by using the hydraulic system. During maintenance procedure while checking the Emergency Release System (ERS) functionality, accidental release of Emergency release coupling can cause fatality. Hence a fool proof design is suggested with an extra locking arrangement. The studies conducted till now and the reviews conducted contributed in the analysis of the development and validation of the design. A design of a locking machanism for preventing the fatality is created and analysed for suggesting it to the industry so that it could be incorperated in the upcoming project of constructing the marine loading and unloading arm. 2023 American Institute of Physics Inc.. All rights reserved. -
Identifying a Range of Important Issues to Improve Crop Production
Crop yield production value update has a beneficial practical impact on directing agricultural production and informing farmers of changes in crop market prices. The main objective of the suggested method is to put the crop selection technique into practise so that it may be used to address a variety of issues facing farmers and the agricultural industry. As a result, the yield rate of crop production is maximised, which benefits our Indian economy. land conditions of several kinds. So, using a ranking system, the quality of the crops are determined. This procedure also alerts farmers to the rate of crops of low and high quality. Due to the use of multiple classifiers, using an ensemble of classifiers paves the way for better prediction decisions. The decision-making process for selecting the output of the classifiers also incorporates a rating system. The price of a crop that will produce more is predicted using this method. 2023 IEEE. -
Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. 2023 IEEE. -
Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. 2022 IEEE. -
Image Processing and Artificial Intelligence for Precision Agriculture
Precision agriculture is a novel approach to increase the productivity of crops that employs recent technologies such as Artificial Intelligence, WSN, cloud computing, Machine Learning, and IoT. This paper reviews the development of different techniques effectively used in precision agriculture. The paper details the technological impact on precision agriculture followed by the different image processing schemes such as Satellite imagery and unmanned aerial vehicle (UAV). The role of precision agriculture is disease detection, weed detection from UAV images, and detection of trees and contaminated soils from satellite imagery is discussed. It reviews the impact of artificial intelligence (AI) namely machine learning &deep learning in precision agriculture. The performance of the recent image processing schemes in precision agriculture is analyzed. The paper also discusses the challenges that exist in implementing the precision agriculture system. 2022 IEEE. -
Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities
Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322. 2022 IEEE. -
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork
The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %. 2022 IEEE. -
Impact of AI in Financial Technology- A Comprehensive Study and Analysis
Presently across the world, financial institutions strive tremendously hard to make financial services smarter to benefit from the advantages of digitization. To enhance client services, financial technology (Fintech) uses a variety of modern breakthrough technologies, including Artificial Intelligence (AI), 5G/6G, Blockchain, Metaverse, IoT, and others, in the financial sector. Many important financial services and procedures, including loans, authentication, fraud detection, quality control, creditworthiness, and several more, would be streamlined and improved by the adoption of technology. However, a need exists for the development of innovative financial products as well as the corresponding technological ecosystem. To launch Information and Communication Technology (ICT) alternatives, various major tech companies have placed their emphasis on Fintech. In this paper, we first explore the latest opportunities in Fintech. Furthermore, we also attempt to present a foundation of the Fintech accelerators, such as IoT, 5G, Digital twins, and Metaverse. Additionally, we also outline recommendations for future research directions in Fintech while looking forwards to potential difficulties. 2023 IEEE. -
Impact of AI Technology Disruption on Turnover Intention of Employees in Digital Marketing
The rapid integration of AI technology into the digital marketing sector has prompted a need to understand its effects on employee perspectives and behaviors. This study investigates how AI adoption influences job insecurity, turnover intention, and job mobility among digital marketing professionals. Addressing concerns about AI rendering roles obsolete is crucial for fostering a supportive work environment. Turnover intention, influenced by AI adoption and potential job dissatisfaction, offers insights into employees' commitment to the industry. Job mobility, influenced by growth prospects and alignment with AI-driven workplaces, sheds light on career aspirations. Our study involving 303 employees of digital marketing industry in India reveals that AI disruption significantly impacts turnover intention, with job insecurity mediating this effect. Additionally, mistreatment by superiors increases turnover intention. Overall, this research underscores the profound impact of AI technology on employees' attitudes, behaviors, and career decisions in digital marketing, providing valuable insights into their perceptions and engagement 2024 IEEE. -
Impact of air pollution in health and socio-economic aspects: Review on future approach
Air contamination is mainly induced by human activity and environmental pollution. Consumption of Air pollution in fewer amounts leads to a significant range of harmful effects on public safety. Nevertheless, with the accelerated pace of economic growth and modernization and the high quantity of electricity need results in huge amounts of pollutants and waste creates significant air pollution. The latest research has shown that because humans only use a tiny part of their day to drive, their constant air quality intake is primarily attributed to the commuting microenvironment. The nature of life on this planet is dependent on clean air. This article presents the literature to include a review of the effect on different facets of human existence of air emissions. The effect is narrowly classified into health and climate change. The study shows that air contamination has a broad variety of consequences, from infectious illnesses and life-threatening disorders and the breakdown of particular organ systems and psychological health. There is no question that this problem has to be addressed with the utmost focus. Such results should be used to prompt further work and to deliver clean air initiatives to officials. 2020 Elsevier Ltd. All rights reserved. -
Impact of Artificial Intelligence on Business Strategy and Decision-Making
Market analysis as knowledge-enhancement function, its use in internal politics, its abuse, and its ability to generate market understanding were recognised as the four key performance variables in market analysis. Profitability and bottom line may be increased, inefficiencies in corporate processes can be reduced, and other hidden insights can be uncovered by analyzing financial accounting transactions. The research focuses on the business strategy in decision-making using artificial intelligence. Reviewing existing research and providing recommendations. In this study, firstly collect the dataset finance data from Kaggle for the better-trained model. After that perform the pre-processing data for outlier removal. The implementation work is complete on the Python programming language. The results showed that the proposed KNN, the Decision tree model, achieved high accuracy. Businesses and organisations working in the field of artificial intelligence (AI) might greatly benefit from this research in terms of narrowing down the profiles that are certain to avoid in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
IMPACT OF ARTIFICIAL INTELLIGENCE ON E-BANKING AND FINANCIAL TECHNOLOGY DEVELOPMENT
Artificial intelligence has a significant impact on financial technologies. Machine learning is an important field of artificial intelligence. Machine learning is a subset of artificial intelligence. According to client knowledge gathered by machine learning, data structures may be more easily comprehended and changed. Machine learning, although still being employed in the IT business, has its own set of benefits. They are used by computer program to explain or solve a typical issue because they are a set of well-written instructions. Data inputs for factual research may be prepared by computers using master learning algorithms that can deliver results within a certain range. Computers are used to model test data, and frameworks are used to make automated decisions based on input data. Banks and financial institutions may benefit from the use of machine learning. This article discusses applications of machine learning in banking and finance sector. The Electrochemical Society