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Security and Privacy in Internet of Things (IoT) Environments
Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE. -
Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
Monetary fraud, which is a deceptive method for getting cash, has turned into a typical issue in organizations and associations as of late. Customary techniques like manual checks and reviews aren't extremely precise, are costly, and consume most of the day. Attempting to get cash by lying. With the ascent of simulated intelligence, approaches based on machine learning have become more well known. can be utilized shrewdly to track down fraud by dissecting an enormous number of monetary exercises information. Thus, this work attempts to give a systematic literature review (SLR) that ganders at the literature in a systematic manner. reviews and sums up the exploration on machine learning (ML)-based fraud recognizing that has proactively been finished. In particular, the review utilized the Kitchenham strategy, which depends on clear systems. It will then, at that point, concentrate and rundowns the significant pieces of the articles and give the outcomes. Considering the Few investigations have been finished to accumulate search systems from well-known electronic information base libraries. 93 pieces were picked, examined, and integrated in light of measures for what to incorporate and what to forget about. As the monetary world gets more confounded, robbery is turning into a more serious issue in the accounting and finance industry. Fraudulent activities cost cash, yet they likewise make it harder for individuals to trust monetary frameworks. To stop this danger, we want further developed ways of tracking down fraud straightaway. This theoretical gives an outline of how machine learning strategies are utilized to further develop fraud detection in accounting and finance. 2024 IEEE. -
Compression Based Modeling for Classification of Text Documents
Classification of text data one of the well known, interesting research topic in computer science and knowledge engineering. This research article, address the classification of text files issue using lzw text compression algorithms. LZW is a lossless compression technique which requires two pass on the input data. These two passes are treated separately as training stage and text stage for classification of text data. The proposed compression based classification technique is tested on publically available datasets. Results of the experiments shows the effectiveness of the proposed algorithm. 2019, Springer Nature Singapore Pte Ltd. -
Energy Management System for EV Charging Infrastructure
The increasing adoption of electric vehicles (EVs) has led to a significant rise in the demand for efficient and sustainable charging infrastructure. Managing the energy supply to meet this growing demand while ensuring grid stability presents a critical challenge. This paper presents an energy management system designed for electric vehicle charging infrastructure that balances demand and supply in real time. The proposed system dynamically allocates available power to connected EVs based on their charging demands and the total power available, ensuring optimal utilization of energy resources. By simulating various scenarios, the system demonstrates its capability to prevent overloading, efficiently distribute power, and prioritize critical energy needs. The results of the simulation show that the system can effectively manage power distribution, reduce peak load impact, and enhance the reliability of EV charging networks. This approach offers a scalable and adaptable solution for integrating EVs into the existing power grid, contributing to the development of smart and sustainable transportation systems. The Authors, published by EDP Sciences. -
Study on Spray Dried Yttria Stabilized Zirconia Dental Implants
Medical implants are devices, tissues or supports that are positioned in a suitable manner on any defective part of the human body to facilitate its smooth functioning again. Known as 'prosthetics', they may be used to offer support to a specific organ or tissues, distribute medication, or observe the body condition. While many of the implants are made from skin, bone or other tissues removed from the body itself, the artificial ones are made from engineering materials which could be any of the compatible metals, plastics, ceramics or even composites. The high end technologically advanced implant material is expected to withstand severe barriers and compatibility issues when in contact with the human body. One such application is dental implants, where, the materials must possess superior mechanical properties, exhibit good hydro-chemical and low thermal degradation characteristics. They are also required to possess characteristics such as low friction, strong wear resistance, good wettability and biocompatibility, when placed in the mouth. The only materials that come close to meeting the needs are ceramics, limited by the associated high fracture rate. Stabilized zirconia (stabilized with yttria, ceria etc.) has provided potential solution. Among the two stabilizers, ceria stabilized zirconia may be a better alternative to yttria stabilized zirconia. Other alternatives are alumina, apatites: but their use are constrained based upon technological and cost considerations. Implant product is a highly demanding technology. Spray drying is a suitable process methodology to obtain free flowing powders with uniform morphology and chemical composition, essential for an implant production. This paper presents (i) results from spray drying 8% Y2O3-stabilized ZrO2 and (ii) a review of published literature pertaining to dental implant materials, the various processing methodologies, with special reference to stabilized zirconia and spray drying. Published under licence by IOP Publishing Ltd. -
From Text to Action: NLP Techniques for Washing Machine Manual Processing
This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals. 2024 Elsevier B.V.. All rights reserved. -
Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE. -
Formation and photoluminescence of ZnS:Tb nanoparticles stabilized by polyethylene glycol
ZnS nanoparticles doped with 1 mol.% of Tb have been prepared at 70 C by simple chemical precipitation method using poly ethylene glycol (PEG) as capping agent. The synthesized nanoparticles have been analysed using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), photoluminescence (PL) and UV-Vis absorption spectroscopy. From X-ray diffraction analysis, it was found that nanostructured ZnS:Tb particles exhibited cubic structure with an average crystallite size of 2.75 nm. Room temperature photoluminescence (PL) spectrum of the doped sample exhibited broad emission in the visible region with multiple peaks at 395 and 412 nm due to 5D3?7F6and 7F5transitions and 492, 536, 600, 653 and 680 nm due to 5D4?7F67F57F4,7F1and 7F0transitions. 2020 Elsevier Ltd. All rights reserved. -
A Comparative Study of Machine Learning and Deep Learning Algorithms to Predict Crop Production
Agriculture is a field that plays an essential part in strengthening a country's economy, especially in agrarian countries like India, where agriculture and crop productivity play a large role in the economy. The research focuses on comparing machine learning and Deep learning algorithms in predicting total crop yield production. The parameters considered for the study are State name, District name, Year, Season, Crop, Area and Production. The dataset is resourced from the data.gov.in website. Random forest from Machine Learning and Sequential model from Deep learning are compared, and the performance metric considered for the study is R2 score. The objective is to assess how well the independent variable predicts the variance in the dependent variable. Random Forest algorithm achieved an R2 score of 0.89, whereas Deep Learning Sequential algorithm gave an R2 score of 0.29. 2023 American Institute of Physics Inc.. All rights reserved. -
Synthesis and characterization of Poly-Vinyl Alcohol-Alumina composite film: An efficient adsorbent for the removal of Chromium (VI) from water
Composite poly vinyl alcohol-alumina films were synthesized by a novel eco-friendly route in the absence of template. The physico-chemical nature of the synthesized film was studied using different characterization techniques. The poly vinyl alcohol-alumina composite film was found to be an efficient adsorbent for the removal of Chromium (VI) at higher concentrations from water. The preparation conditions were optimized to synthesize an efficient adsorbent film for the removal of chromium. The surface properties, chemical composition and amorphous nature of the film confirmed by different characterisation techniques attributes to the chromium removal efficiency of the film. Poly vinyl alcohol-alumina films are economically cheap, easy to prepare, efficient adsorbent for removal of chromium (VI) eco-friendly in nature and reusable with effortless regeneration methods. 2022 -
Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms
The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool. 2021 IEEE. -
RNA-seq DE genes on Glioblastoma using non linear SVM and pathway analysis of NOG and ASCL5
Differentially Expressed genes related to Glioblastoma Multiforme as an output of RNASeq studies were further studied to conclude new research insights. Glioma is a type of intracranial tumor (within the skull), which can grow rapidly in its malignant stages. Gene expression in Grade II, III and IV Gliomas is analysed using non linear SVM models. The enriched GO terms were identified GOrilla. Pathways related to NOG and ASCL5 gene were studied using Reactome. 2020, Springer Nature Switzerland AG. -
Financial Market Forecasting using Macro-Economic Variables and RNN
Stock market forecasting is widely recognized as one of the most important and difficult business challenges in time series forecasting. This is mainly due to its noise. The use of RNN algorithms for funding has attracted interest from traders and scientists. The best technique for learning long-term memory sequences is to use long and short networks. Based on the literature, it is acknowledged that LSTM neural networks outperform all other models. Macroeconomics is a discipline of economics that studies the behavior of the economy as a whole. Macroeconomic factors are economic, natural, geopolitical, or other variables that influence the economy of a country. This study studies and test several macroeconomic variables and their significance on stock market forecasting. In macroeconomics, we have series that are updated once a month or even once a quarter, with data that is rarely more than a few hundred characters long. The amount of data given can sometimes be insufficient for algorithms to uncover hidden patterns and generate meaningful results. Depending on the prediction needs, we proposed a feasible LSTM design and training algorithm. According to the findings of this study, the inclusion of macroeconomic variable has a significant impact on stock price prediction. 2022 IEEE. -
A Survey of Traditional and Cloud Specific Security Issues
The emerging technology popularly referred to as Cloud computing offers dynamically scalable computing resources on a pay per use basis over the Internet. Companies avail hardware and software resources as service from the cloud service provider as opposed to obtaining physical assets. Cloud computing has the potential for significant cost reduction and increased operating efficiency in computing. To achieve these benefits, however, there are still some challenges to be solved. Security is one of the prime concerns in adopting Cloud computing, since the user's data has to be released from the protection sphere of the data owner to the premises of cloud service provider. As more Cloud based applications keep evolving, the associated security threats are also growing. In this paper an attempt has been made to identify and categorize the security threats applicable to Cloud environment. Threats are classified into Cloud specific security issues and traditional security attacks on various service delivery models of Cloud. The work also briefly discusses the virtualization and authentication related issues in Cloud and tries to consolidate the various security threats in a classified manner. Springer-Verlag Berlin Heidelberg 2013. -
A mobile based remote user authentication scheme without verifier table for cloud based services
The emerging Cloud computing technology, offering computing resources as a service is gaining increasing attention of both the public and private sector. For the whole hearted adoption of Cloud, the service providers need to ensure that only valid users gain access to the services and data residing within the provider's premises. Ensuring secure access to sensitive resources within the Cloud requires a strong user authentication mechanism using multiple authentication factors. The mechanisms should also consider the increasing needs of Internet access through smart phones and other mobile devices and facilitate access through a variety of devices. Traditionally, a user needs to maintain separate user accounts for each Service Provider whose service he/she desires to use and this may cause inconvenience to users. Single Sign on (SSO) addresses this issue by permitting users to create one login credential and access multiple services hosted in different domains. In this scenario, a compromise of the single credential can result in account take over at many other sites. This points out to the requirement of strengthening the authentication mechanism by using more than one factor. This paper proposes a SSO based remote user authentication scheme for a Cloud environment. The proposed protocol uses password and mobile token and does not require the server to maintain a verifier table. The protocol is verified using automated security Protocol verification tool, Scyther and the results prove that the protocol provides protection against man-in-the-middle attack, replay attack and secrecy of the user's credentials. 2015 ACM. -
A Single Sign on based secure remote user authentication scheme for Multi-Server Environments
A Multi-Server Architecture comprises of a server environment having many different servers which provides the user the flexibility of accessing resources from multiple Service Providing Servers using the same credential. The primary objective of a Multi Server Environment (MSE) is to provide services of different Service Providers (SPs) without repeating registration at each SP server, and to get a unique single credential for all the servers in MSE. However, the conventional MSEs, proposed by various researchers, proposes the individual authentication service by each SP on their respective server using the credential issued by the Registration Authority of MSE. The mechanism requires the user to access each SP by keying the same credentials for every SP separately. Single Sign On (SSO) is an authentication mechanism that enables a user to sign-on once and access the services of various SPs in the same session. SAML is generally used as a Single Sign-On protocol. This work analyzes the smart card based authentication scheme for Multi-Server Environment proposed by Li et al.'s and discuss various security attacks on the said scheme. The paper also proposes a Secure Dynamic-ID based scheme using smart cards or crypto cards which do not require a verifier table and implements Single Sign On feature using SAML protocol, thus allowing the user to enjoy all the features of an MSE along with SSO. 2014 IEEE. -
A proof of concept implementation of a mobile based authentication scheme without password table for cloud environment
Cloud computing is a fast growing technology offering a wide range of software and infrastructure services on a pay-per-use basis. Many small and medium businesses (SMB's) have adopted this utility based Computing Model as it contributes to reduced operational and capital expenditure. Though the resource sharing feature adopted by Cloud service providers (CSP's) enables the organizations to invest less on infrastructure, it also raises concerns about the security of data stored at CSP's premises. The fact that data is prone to get accessed by the insiders or by other customers sharing the storage space is a matter of concern. Regulating access to protected resources requires reliable and secure authentication mechanism, which assures that only authorized users are provided access to the services and resources offered by CSP. This paper proposes a strong two-factor authentication mechanism using password and mobile token. The proposed model provides Single Sign-on (SSO) functionality and does not require a password table. Besides introducing the authentication scheme, the proof of concept implementation is also provided. 2015 IEEE. -
A Quantitative Analysis of Trading Strategy Performance Over Ten Years
This study conducts a comparative analysis of two trading strategies over a ten-year period to assess their profitability and risk. Strategy 1 operates on a simple buy at close and sell at open principle, while Strategy 2 trades only when the closing price is above the 200-day moving average, introducing a conditional filter for market entry. Through the evaluation of performance metrics including total PNL, drawdown, standard deviation, and Sharpe ratio, the research highlights the differences in risk and return between the strategies. Results indicate Strategy 1 achieves higher profitability but at the cost of greater risk, as shown by larger drawdowns. Conversely, Strategy 2's conditional approach yields slightly lower returns but demonstrates a superior risk-adjusted performance. The findings emphasize the significance of risk management and the potential benefits of conditional filters in trading strategies, offering valuable insights for traders and investors in making informed strategy selections. 2024 IEEE. -
Perception and Practices of EdTech Platform: A Sentiment Analysis
Virtual and digital learning being the new normal, pandemic outburst and unexpected disruption in the functioning of educational services have paved way for online learning services. Considering the fast-Track growth of the education technology (EdTech) industry, in order to sustain, it is imperative for the industry to understand the underlying issues by capturing the end users' perception. The primary purpose of this research is to examine the perception of users towards EdTech platforms A sample of 600 reviews regarding three major EdTech platforms were scraped from MouthShut.com as textual data and analysed using lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analysed using sentiment analysis. Furthermore, the topic modelling on the reviews was performed using natural language programming. The results revealed a positive sentiment of users towards the EdTech services and platforms. The most influential factors are faculty expertise, interface user-friendliness, syllabus, and pricing model. Our findings help EdTech service providers to understand which factors are driving this dramatic shift in student behaviour so they may develop better strategies to attract and retain consumers. Despite the rise in EdTech platform popularity, this is the first study to investigate perception of EdTech users comprehensively. 2022 IEEE. -
Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement
The research intends to find how students' health and academic performance are affected by their smartphone use. Considering how widely smartphones are used among students, it is important to know how they could affect health and learning results. This study aims to create prediction models that can spot trends and links between smartphone usage, health ratings, and academic achievement, thereby offering insightful information for teachers and legislators to encourage better and more efficient use among their charges. Data on students' mobile phone use, health evaluations, and academic achievement were gathered for the study. Preprocessing of the dataset helped to translate categorical variables into numerical forms and manage missing values. Trained and assessed were many machine learning models: Random Forest, SVM, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN). The models' performance was evaluated in line with their accuracy in influencing performance effects and health ratings. Predictive accuracy was improved by use of feature engineering and model optimization methods. With 63.33% of accuracy for estimating health ratings, the SVM model was most successful in capturing the link between smartphone usage and health results. With an accuracy of 50%, logistic regression performed very well in forecasting performance effect, therefore stressing important linear connections between consumption habits and academic success. Random Forest and Decision Tree models were less successful for performance impact even if they showed strong performance in health forecasts. These results highlight the need of customized treatments to reduce the detrimental consequences of too high mobile phone use on students' academic performance and health. 2024 IEEE.