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Technologies in Transportation Engineering
Deteriorating quality of the air, traffic congestions, and rising accident rates have all resulted from an ever-increasing number of vehicles in Indian cities. As a result of a variety of issues, current public transit systems often fall short or are considered unreliable. The present paper deals with multiple ITS architecture and to be specific four major parts of the ITS. These four major parts are Advanced Public Transportation System (APTS), Advanced Traveler Information System (ATIS), Advanced Traffic Management System (ATMS), and Emergency Management System (EMS). Thus, the framework and produced models of four key divisions of ITS have been evaluated in order to conduct a comparative study of the many models currently being developed in respective investigations. 2022 IEEE. -
CoInMPro: Confidential Inference and Model Protection Using Secure Multi-Party Computation
In the twenty-first century, machine learning has revolutionized insight generation by using historical data across domains like health care, finance, and pharma. The effectiveness of machine learning solutions depends largely on the collaboration between data owners, model owners, and ML clients, without privacy concerns. The existing privacy-preserving solutions lack efficient and confidential ML inference. This paper addresses this inefficiency by presenting the Confidential Inference and Model Protection, also known as the CoInMPro, to solve the privacy issue faced by model owners and ML clients. The CoInMPro technique is suggested with an aim to boost the privacy of model parameters and client input during ML inference, without affecting the accuracy and by paying a marginal performance cost. Secure multi-party computation (SMPC) techniques were used to calculate inference results confidentially after sharing client input and model parameters privately from different model owners. The technique was implemented in Python language using the open-source SyMPC library to support the SMPC function. The Boston Housing Dataset was used, and the experiments were run on Azure data science VM using Ubuntu OS. The result suggests CoInMPros effectiveness in addressing privacy concerns of model owners and inference clients, with no sizable impact on accuracy and trade-off. A linear impact on performance was noted with an increase of secure nodes in the SMPC cluster. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Growth and characterization of glycine potassium nitrate NLO crystals
Single crystals of glycine potassium nitrate were grown using slow evaporation technique. The solutions were prepared mixing glycine with potassium nitrate in different ratios stirring continuously for an hour to get a saturated solution. It was then kept at room temperature for controlled evaporation. Optically clear and well shaped crystals were obtained and these were characterized by (FTIR) studies, EDAX and X-ray powder diffraction. 2011 American Institute of Physics. -
Power quality improvement strategy for non-linear load in single phase system
Widespread use of non-linear loads in today's world scenario, increased the harmonic current injection into the grid. The harmonic current play a vital role in deteriorating the power quality of the grid. The non-linear loads may be either, single phase or a Three phase loads. In this paper, a control strategy for single phase shunt active filter is discussed, in mitigating the harmonics flowing into the grid. The extraction of reference signal of shunt active filter is designed, using instantaneous reactive power theory. Here load is considered as diode rectifier which is feeding a resistive inductive load. A complete control strategy and analysis is done in MATLAB/Simulink environment. 2016 IEEE. -
The Empirical Analysis of Machine Learning Approaches for Enhancing the Cyber security for better Quality
In recent years, there have been significant advances in both technologies tactics so in area of cyber security, with (ML) machine learning at the forefront of the transformation. It is the ability to obtain security event characteristics or findings from cyber security information and then develop a matching information model that will allow a security system to become autonomous and smart. The widespread proliferation and the usage of Web and Smartphone applications has increased the size of cyber world as a consequence. When a computerized assault takes too long to complete, the internet becomes vulnerable. Security measures may be improved by recognizing and reacting to cyber-attacks, thanks to cyber security techniques. Security measures that were previously used aren't any longer appropriate because scammers have learned how to evade them. It is getting more difficult to detect formerly unknown and unpredictable security breaches, which are growing more widespread. Cyber security is becoming more dependent on machine learning (ML) techniques. Machine learning algorithms' dependability remains a major challenge, given its continual advancement. It is possible to find malicious hackers in internet that are ready to exploit ML defects that have been made public. A thorough review of machine learning techniques safeguarding cyberspace against attacks is provided in this paper, which presents a literature review on Cyber security using machine learning methods, such as vulnerability scanning, spam filtering, or threat detection on desktop networks as well as smart phone networks. Among other things, this paper provides brief descriptions of each machine-learning technique and security info, essential machine-learning technology, and evaluation parameters for a classification method. 2022 IEEE. -
Machine Learning Insights into Predicting Crude Oil Prices
This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. 2024 IEEE. -
Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE. -
Human Resource Management in the Power Industry Using Fuzzy Data Mining Algorithm
Currently, database and information technology's frontier study area is data mining. It is acknowledged as one of the essential technologies with the greatest potential. Numerous technologies with a comparatively high level of technical substance are used in data mining, including artificial intelligence, neural networks, fuzzy theory, and mathematical statistics. The realization is challenging as well. Job satisfaction is one of several factors that cause employees to leave or switch jobs, and it is also closely tied to the organization's human resource management (HRM) procedures. It is continuously difficult and at times beyond the HR office's control to keep their profoundly qualified and talented specialists, yet data mining can assume a part in recognizing those labourers who are probably going to leave an association, permitting the HR division to plan a mediation methodology or search for options. We have analysed the major thoughts, techniques, and calculations of affiliation rule mining innovation in this article. They effectively finished affiliation broadcasting, acknowledged perception, and eventually revealed valuable data when they were coordinated into the human resource management arrangement of schools and colleges. 2023 IEEE. -
Analyzing the Consumer Buying Behavior by Adapting Artificial Intelligence (AI)
In any business consumers or customers are important part of the market, so it is necessary to attract more customers for increasing the profits. The current research in this area has demonstrated that artificial intelligence (AI) has a substantial impact on the end customer, contrary to the widespread notion that it has more of an impact on industry than other manufacturers. There are many studies on the various applications of AI in analyzing and visualizing the consumer behavior. Thus, it is been observed the behavior of consumer is not same for same businesses, it varies from consumer to consumer. In other respects, AI is changing how consumers act right now. In coming year's use of AI will become common where the human dependable businesses also get automated with time. 2024 IEEE. -
Impact of Artificial Intelligence on the Social Media Marketing Strategies
The use of social media is increasing as the use of smartphones is increasing, various applications on smartphones are now becoming good platform for market. As the use of smartphones is increasing the use of different artificial intelligence (AI) technologies making the phones smarter. The social media is now one of the most globally crowded platforms with millions of users. Most of the businesses are now turning their marketing strategies by highlighting the digital marketing from various platforms. Thus, the focus of study is to find the increasing impact of AI on the social media related marketing strategies. Different studies highlight the different impacts of social media and marketing using different AI tools and platforms which makes customer to find the best product as per their choice. So, social media marketing has become simpler and more adaptable thanks to the development of artificial intelligence. 2024 IEEE. -
Cognitive Engagement Scale (CES) in an Online Environment: Construction and Validation
Researchers have demonstrated linkages between active engagement of students with learning material and greater learning gains. Cognitive engagement is a significant component of educational experience. Understanding the challenges associated with cognitive engagement and measuring cognitive engagement in a MOOC environment is challenging. It is the need of the hour with online learning being equivalent to classroom learning in todays dynamic academic environment. The present study aims to construct cognitive engagement scale (CES) to measure the cognitive engagement of learners who sign up for the massive open online courses (MOOC). The aim of this study is dual-fold: firstly, to conceptualize the cognitive dimension of learner engagement within MOOCs, and secondly, to construct a theoretically informed scale for assessing cognitive engagement in online environments. Study presents a detailed process of the scale development, which included item generation, item evaluation, pilot testing, testing psychometric properties of the scale, and scale refinement. The researchers crafted the initial questionnaire drawing from both existing literature and personal insights. Subject matter experts then validated the items within the questionnaire and ensured its reliability through a pilot study, where it was administered to a sample of 100 participants The final version of the scale captures the four dimensions of cognitive engagement: Passive receiving, active manipulating, constructive generating, and interactive dialoguing. The present study contributes to the growing literature on cognitive engagement and adds to the existing literature of MOOC engagement scale with focus on cognitive engagement exclusively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies
This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. 2024 IEEE. -
A Comparative Study of Spectral Indices for Surface Water Delineation Using Landsat 8 Images
Surface water delineation is an important step in performing change detection studies on water bodies with the help of multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral unmixing based methods. This paper presents a comparative study of commonly used spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Forest Index (NDFI), Enhanced water Index (EWI), Weighted Normalized Difference Water Index (WNDWI), Automated Water Extraction Index (AWEI), Tasseled Cap Water Index (TCW), Global Water Index (GWI)and Sum457 that were developed for water detection for their suitability and effectiveness when applied on Landsat 8 images. While all the above mentioned indices showed their usefulness in water detection, simpler and faster indices like GWI and Sum457 yielded comparable results to that of more complex ratios like EWI and WNDWI. 2019 IEEE. -
Object Detection with Augmented Reality
This study describes an artificial intelligence (AI)-based object identification system for detecting real-world items and superimposing digital information in Augmented Reality (AR) settings. The system evaluates the camera stream from an AR device for real-Time recognition using deep learning algorithms trained on a collection of real-world items and their related digital information. Object recognition applications in AR include gaming, education, and marketing, which provide immersive experiences, interactive learning, and better product presentations, respectively. However, challenges such as acquiring larger and more diverse datasets, developing robust deep learning algorithms for varying conditions, and optimizing performance on resource-constrained devices remain. The AI-based object recognition system demonstrates the potential to transform AR experiences across domains, while emphasizing the need for ongoing research and development to fully realize its capabilities. 2023 IEEE. -
Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE. -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd.