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PE-v-SVR based Architecture to Predict and Prevent Low and Slow-Rate DDoS Attacks using Machine Learning
Distributed Denial of Service (DDoS) attacks continue to emerge; low and slow attacks pose a serious threat. These small-scale attacks often evade traditional security protections and increase the risk of long-term outages and loss of service. Our research aims to develop effective predictive models and strategic defences to detect and mitigate slow DDoS attacks. The proposed model combines Power Spectral entropy and V-Support Vector Regression. More importantly, the version achieves the first-class error price in the variety of zero to at least one, demonstrating its effectiveness in detecting and predicting DDoS attacks. Research results show the effectiveness of the proposed design using PSD (power spectral density) entropy and V-SVR. The best mean square error obtained further confirms the ability of the model in this context. V-SVR in low and sluggish DDoS assaults. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE. -
Beyond Automation: Understanding the Transformational Capabilities of AI in Management
The investigation explores at the various ways that artificial intelligence (AI) is affecting management techniques. The study highlights the dichotomy between automation and augmentation, highlighting how artificial intelligence (AI) can replace human work through automation, but its ultimate use in augmenting human capabilities (augmentation) leads to better organisational performance. This analysis reveals how AI-driven tactics enhance operational efficiency, decision-making, and productivity by synthesising research findings from a variety of domains, including manufacturing, banking, municipal sectors, and remote work environments. It also looks at how AI may change management through big data and data analytics, recommending a shift to an integrated strategy that combines automation and human understanding to promote creativity and long-term growth. 2024 IEEE. -
Optimal Feature Selection for the Classification of Hyperspectral Imagery Using Adaptive SpectralSpatial Clustering
Hyperspectral images captured through the hyperspectral sensors play an imperative part in remote sensing applications in the present context. Unlike traditional images sensed with few bands in the visible spectrum, the hyperspectral (HS) images are obtained with hundreds of spectral band ranges from infrared to ultraviolet regions. Because of its vast spatial and spectral data, it requires an extensive computational system for processing and its hidden features are needed to be unveiled in an effective manner specifically for the classification of HS imagery. This approach exploits the high spectral band correlation and rich spatial information of the HS images for the generation of feature vectors. To attain optimal feature space for the best probable classification, an adaptive approach is incorporated to adaptively choose spectralspatial features for feature selection to classify the pixels effectively. Furthermore, the HS image encompasses several bands including noisy bands. To categorize the images with great accuracy, it is suggested to eradicate the noisy bands whilst retaining the informative bands. In this research, an adaptive spectralspatial feature selection scheme is proposed for HS images where the extremely correlated representative bands are considered for analysis with uncorrelated and noisy spectral bands are judiciously discarded during its classification process. This hybrid approach not merely diminishes the computational time and also improves the general classification accuracy significantly. The empirical result displays that the proposed work surpasses the conventional approach of HS image classification systems. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Rights based approaches to poverty reduction and development reality versus rhetoric
Over the past two decades erudite understanding of poverty has generated an overlapping consensus on what poverty entails. It is now almost universally accepted that poverty is multi-dimensional, and is a human rights violation that arises mainly from structural inequalities. The search for a holy grail of its reduction has seen widespread deployment of Rights-based newlineapproaches (RBAs), fronted by NGOs, since the turn of the century. In spite of this, coupled with a marked increase in development resources, poverty is proving to be robustly sustainable. The study determined the appropriateness and effectiveness of RBAs newlineas a guiding framework for sustainable poverty reduction and development. This entailed an assessment of the practical impact of RBAs and implementation of RBA strategies as well as identification of key variables necessary for successful rights-based development. As a descriptive survey, the study was underpinned by the pragmatism research philosophy, and employed a mixed methods approach with a concurrent embedded strategy that was largely qualitative but embedding a quantitative strand. Data were collected through interviews, observations and focus group discussions. In all 98 newlineparticipants from 25 villages and 9 organisations were directly studied newline(excluding observations) and were selected using probability and nonprobability sampling methods. Data were analysed using the thematic approach and SPSS. The results of the study highlighted that poverty which had increased during the period covered by the study, is still largely defined from the basic needs and income perspectives, and attributed to individual deficiencies. newlineUnderstanding of RBAs is weak and orientation on RBAs to staff and partners was inadequate. While the quality of development programs improved under RBAs, the quantity and distribution of development outputs and outcomes did not improve. -
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AI Applications Computer Vision and Natural Language Processing
Artificial intelligence (AI) applications in computer vision and natural language processing (NLP) have made major advances in recent years, challenging a number of sectors and areas. This multidisciplinary topic combines NLP, which examines the study of human language, and computer vision, which concentrates on the understanding of visual data. This study examines the wide range of applications that are included within this convergence, highlighting the revolutionary potential of AI technology. AI has made it possible to make significant advances in autonomous systems, object identification, and image recognition in the field of computer vision. These developments have stimulated innovation and increased efficiency, revolutionizing sectors including healthcare, autonomous vehicles, and security. Meanwhile, AI-driven advances in NLP have produced strong language models that can produce, comprehend, and translate text. These approaches have been utilized to improve accessibility and efficiency of communication in chatbots, sentiment analysis, and language translation services. This chapter explores the basic ideas and advancements in these two fields, emphasizing the opportunities and novel challenges that arise from integrating computer vision and NLP. Additionally covered are data privacy, ethical issues, and the possibility of prejudice in AI applications. The study also highlights the ongoing need for these fields' advancement and investigation in order to solve real-world problems and fully utilize AI's potential in the computer vision and NLP industries. 2025 The Institute of Electrical and Electronics Engineers, Inc. -
Deep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Disease
After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lattice thermal conduction in cadmium arsenide
Lattice thermal conductivity (LTC) of cadmium arsenide (Cd3As2) is studied over a wide temperature range (1-400 K) by employing the Callaway model. The acoustic phonons are considered to be the major carriers of heat and to be scattered by the sample boundaries, disorder, impurities, and other phonons via both Umklapp and normal phonon processes. Numerical calculations of LTC of Cd3As2 bring out the relative importance of the scattering mechanisms. Our systematic analysis of recent experimental data on thermal conductivity (TC) of Cd3As2 samples of different groups, presented in terms of LTC, ? L, using a nonlinear regression method, reveals good fits to the TC data of the samples considered for T < ? 50 K, and suggests a value of 0.2 for the Gruneisen parameter. It is, however, found that for T > 100 K the inclusion of the electronic component of TC, ? e, incorporating contributions from relevant electron scattering mechanisms, is needed to obtain good agreement with the TC data over the wide temperature range. More detailed investigations of TC of Cd3As2 are required to better understand its suitability in thermoelectric and thermal management devices. 2022 Chinese Physical Society and IOP Publishing Ltd. -
Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks
The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms. 2013 IEEE. -
A Comprehensive Model for Forecasting the Nifty50 Index Using MAchine and Deep Learning Methodologgy with Reference to National Stock Exchange
The volatility and uncertainty make stock and stock price index predictions challenging. Many financial professionals and academics are interested in stock price/index prediction studies. This study presents computational ML and DL intelligence techniques for estimating the NIFTY50 index closing value on the Indian NSE using Fundamental Analysis and Technical Analysis. To forecast the NIFTY50 index, we first employed Fundamental Analysis and max voting, bagging, boosting, and stacking ensemble learning techniques. An embedded feature selection algorithm is utilized to determine the model's best fundamental indicators, and a grid search is performed to tweak hyperparameters for each base regressor. Our results demonstrate that the bagging and stacking regressor model 2 beat all other models, with the lowest RMSE of 0.0084 and 0.0085, respectively, indicating an improved fit of ensemble regressors. Subsequently, TA research was done to exhibit the influence of deep learning on the NIFTY50. This method employs a data augmentation mechanism and three GRU model variations. It is examined using two datasets, TA1 and TA2, which include technical indicators from the NIFTY50 index. The GRU model enhanced the NIFTY50 index prediction using the TA1 technical indicator dataset. Finally, the study examines a hybrid model to estimate equity market trends, combining PCA with ML methods such as ANN, SVM, NB, and RF. The proposed approach uses the trend deterministic data preparation layer to convert the continuous data to a discrete form denoted by +1 or -1. The empirical findings of this hybrid model demonstrate that the RF model with the first three principal components obtains precision of 0.9969, F1-score 0.9968 and AUC score of 1. Overall, the suggested research design outperforms baseline models in our experiments and shows promising results using fundamental and technical analysis indicators. Thus, this study provides an ideal tool for stock market prediction and financial decision-makers. -
Post Covid Scenario Effective E-Mentoring System in Higher Education
During Covid-19 pandemic many people and institutions preferred online coaching instead of in person education. The problem with online is that it will be difficult to carry on interconnections between students and professors in that environment. The main constraint for conducting online session is that the people in remote areas may find a difficulty to connect to online sessions having network issues. Electronic mentoring (e-mentoring) is implemented like a website in which the mentor and mentee can communicate with each other. With the help of this mentoring the project can provide a best solution for both the mentor and mentee. They can communicate with each other with the help of online platform and even with the help of emails.This proposed method will help them to keep the track of their academic progress and achievements of students. This article mainly focus on the mentoring through physical and virtual environment in which the mentee will be interacting with the mentor to know the progress of their academics. This article discusses about the website which is developed to fulfill the needs of the student and it discusses about the various stages of development that helped in building the website. Students can share their difficulties and their achievements with the mentor who are assigned for them particularly. In future planning to implement artificial intelligence technique to online mentoring process, this is for the betterment of student's growth. 2023 IEEE. -
Total Global Dominator Coloring of Trees and Unicyclic Graphs
A total global dominator coloring of a graph G is a proper vertex coloring of G with respect to which every vertex v in V dominates a color class, not containing v and does not dominate another color class. The minimum number of colors required in such a coloring of G is called the total global dominator chromatic number, denoted by Xtgd (G). In this paper, the total global dominator chromatic number of trees and unicyclic graphs are explored. 2023 University of Baghdad. All rights reserved. -
Total domination coloring of graphs
A total domination coloring of a graph G is a proper coloring of G in which open neighbourhood of each vertex contains at least one color class and each color class is dominated by at least one vertex. The minimum number of colors required for a total domination coloring of G is called the total domination chromatic number of G and is denoted by ctd(G). In this paper, we study the total domination chromatic number of some graph classes. The bounds of total domination chromatic number with respect to the graph parameters such as the domination number, chromatic number, total dominator chromatic number and total domination number are also studied. 2021 the author(s). -
IoT Enabled Energy Optimization Through an Intelligent Home Automation
The benefit of IoT devices is that they allow for automation; nevertheless, billions of connected devices connected with one another waste a substantial amount of energy. IoT systems will have difficulty in wide adoption if the energy requirements are not adequately managed. This study proposes a solution for IoT devices to regulate their energy consumption. Both hardware and software aspects are taken into consideration. Using a mobile computer or smartphone with Internet connectivity to interact with actual scenarios has grown more prevalent as technology has advanced over the years. An intelligent home automation system based on android applications has been developed to save electricity and human energy. This study aims to create comprehensive Energy optimization through intelligent home automation utilizing widely available mobile applications and Wi-Fi technologies. The devices are turned on and off using Wi-Fi. Intelligent home, in the area of electronics, automation is the most purposely misused term. Numerous technological revolutions have occurred as a result of this demand for automation. These were more essential than any other technologies due to their ease of use. These can be used in place of household current switches, resulting in sparks and, in rare instances, such as fires. A unique energy optimization system was developed to control household appliances while taking advantage of Wi-Fi benefits. 2023, Bentham Books imprint. -
HCI Authentication to Prevent Internal Threats in Cloud Computing
Cloud computing reduces physical resources and simplifies common management tasks. Over the past decade, cloud computing has become an important IT (information technology) industry, driving cost savings, flexibility, convenience, and scalability. Despite these advantages, many government organizations and companies are still cautious about using cloud computing. They continue to believe that the threats inherent in cloud computing technology are greater and deadly than traditional technologies. Cloud computing security threats typically include insider attacks, malware attacks, information leaks and losses, distributed denial of service, and application programming interface vulnerability attacks. Technical security improvements for virtual networks are actively researched, and many are working hard. But defending against internal attackers is more than just a technical solution but a complement to manuals and company policy. In reality, however, there are cases of damage by internal attackers, and the damage is getting bigger. Technically malicious internal attackers can relatively easily manipulate the control system and cause malfunctions. This paper provides comprehensive information about security threats in cloud computing, shows the severity of attacks by insiders, analyzes the latest authentication technologies for humancomputer interaction, and identifies the pros and cons. This shows how HCI (humancomputer interaction) technology can be applied to cloud computing management servers. The result is an innovative security certification model that can be applied. 2020, Springer Nature Switzerland AG. -
Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques
Drought is a natural phenomenon that puts many lives at risk. Over the last decades, the suicide rate of farmers in the agriculture sector has increased due to drought. Water shortage affects 40% of the world's population and is not to be taken lightly. Therefore, prediction of drought places a significant role in saving millions of lives on this planet. In this research work, six different supervised machine learning (SML) models namely support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent neural networks (RNNs) are compared and analyzed. Three dimensionality reduction techniques principal component analysis (PCA), linear discriminant analysis (LDA), and random forest (RF) are applied to enhance the performance of the SML models. During the experimental process, it is observed that RNN model yielded better accuracy of 88.97% with 11.26% performance enhancement using RF dimensionality reduction technique. The dataset has been modeled using RNN in such a way that each pattern is reliant on the preceding ones. Despite the greater dataset, the RNN model size did not expand, and the weights are observed to be shared between time steps. RNN also employed its internal memory to process the arbitrary series of inputs, which helped it outperform other SML models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.