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Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE. -
Self lubricating property of MWCNT in AA2219 composites during high energy ball milling
Revolutions in nanotechnology enabled the development of advanced nanocomposites with superior properties for engineering applications especially in automotive and aerospace industries. Among this carbonaceous nano materials like MWCNT have got more attention. Addition of MWCNT in metal matrix results in retardation of friction coefficient and improvement on other mechanical properties based on its dispersion. MWCNT won't have sufficient space to occupy over the powder surface, when the addition is beyond a limit and acts as a solid lubricant during milling. Investigations on self lubricating property during milling were done by using scanning electron microscope, X-ray diffraction and powder density. Uniform dispersion was the bottleneck to utilize their attractive properties of the reinforcement. An attempt had been done for a uniform dispersion during premixing process using a combination of ultra-sonication, magnetic and mechanical stirring followed by high energy ball milling. 2019 Elsevier Ltd. -
Self Risk Assessment Model Embedded with Conversational User interface for Selection of Health Insurance Product
In this research, we propose a dynamic model that works through Human-Computer Interaction to facilitate a smooth customer experience for health insurance prospects. The model facilitates the prospects to self assess their health risks. The integration of Conversational User interface, such as Mobile User Interface, Graphic User Interface and Bots with transcoder permits seamless use of the model by any category of prospects, irrespective of their language. Moreover, the model also helps the visually impaired person to interact without any hassle with the presence of a transcoder that permits conversion of text into speech and vice versa. The learner model comprises of the Prospects' detail module and Risk Assessment modules. The Prospects' detail module collects data from the predefined list. The risk assessment module profiles and assesses the risk based on the data inputted in the Prospects' detail module. The risk assessment level module categorizes the level of risk as low, moderate or high for each prospect depending on the total risk exposure level. The total risk exposure level is computed based on the pre-defined threshold. This model aids the prospect in determining the risk level and thereby facilitates self-selection of health insurance policy, thus reducing over reliance on the insurer. This model helps the prospect to take an independent purchase decision. 2022 IEEE. -
Self-adaptive Butterfly Optimization for Simultaneous Optimal Integration of Electric Vehicle Fleets and Renewable Distribution Generation
Fuel prices and environmental concerns have prompted an increase in the use of electric vehicle (EV) technology in recent years. Charging stations (CSs) are a great way to support this shift to sustainability. This has increased the demand for EV charging on electrical distribution networks (EDNs). However, optimal EV charging stations along with renewable energy sources (RES) integration can maintain EDN performance. This paper proposes a novel hybrid approach based on self-adaptive butterfly optimization algorithm (SABOA) for optimal integration of EV CSs and RES problems under various EV load growth scenarios. A multi-objective function is created from distribution losses, GHG emissions, and VSI. The ideal locations for CSs and RES are found using SABOA while minimizing the proposed multi-objective function. The simulation results on IEEE 33-bus EDN validate the suggested technique's superiority in terms of global optima. This type of hybrid strategy is required for optimal real-time integration of EV CSs and RES, taking into account emerging high EV load penetrations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Sensitivity and tolerance analysis of 2D Profilometer for TMT primary mirror segments
The primary mirror (M1) of Thirty Meter Telescope (TMT) consists of 492 segments of which, 86 are ground and polished by India-TMT. These segments are off-Axis and aspheric in nature and one of the effective methods to polish such segments is through Stressed Mirror Polishing (SMP). During SMP, consistent in-situ metrology of the surface is needed to achieve the required profile. A 2D Profilometer (2DP) will be used by India-TMT for the low frequency profile metrology. The 2DP is a contact-Approach metrology, consisting of probes positioned in a spiral pattern, measuring the sag of segment surface. Initial section of this paper deals with the sensitivity and tolerance analysis of the 2DP. This is followed by the study on position and rotational errors of the 2DP as a whole. Simulation of these analysis is carried out initially on a sphere and then on different segments of the TMT, in order to study the induced measurement errors. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
Sentiment Analysis for Online Shopping Reviews Using Machine Learning
Everyday shoppers need reliable and insightful reviews of e-commerce websites to enhance their shopping experience. This research study explores sentiment analysis on Amazon reviews. It utilizes them as a diverse repository of customer opinions by unlocking their embedded sentiments, thereby recognizing their pivotal role in guiding potential buyers. Sentiment misinterpretations may result from many machine learning models that have trouble comprehending the context of Amazon reviews, particularly regarding subtle wordings, sarcasm, or irony. Additionally, these models can have biases that skew sentiment analysis results, mainly when working with a diverse set of Amazon review datasets. To overcome these, three machine learning models, namely, Bidirectional Encoder Representations from Transformers (BERT), Bidirectional and Auto-Regressive Transformers (BART), and Generative Pre-trained Transformers (GPT) are used in this study. During the experimental research, it was observed that BERT gave the highest accuracy of 90% when compared with BART (70%) and GPT (84%) models. BERTs bidirectional contextual comprehension at identifying subtleties in language provides a thorough and realistic representation of the sentiments of Amazon users, which is why the model gave the highest accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Sentiment Analysis of Lenders Motivation to Use a Peer-To-Peer (P2P) Lending Platform: LenDenClub.Com
Peer-To-Peer lending platforms are becoming a good investment avenue for lenders to invest their money in borrowers, who need money for a different purpose. As lending and borrowing of money is facilitated by the P2P lending platform, it becomes necessary for the platform to understand the users and accordingly fine tune the 'User Interface' (UI) and 'User Experience' (UX) of the platform. For lending and borrowing to take place through a platform it is necessary to have an 'n' number of lenders who are ready to lend money to an 'x' number of borrowers. This study is specifically done to understand lenders' motivation to use P2P lending platforms. This is a unique research work as sentiment analysis of lenders' motivation to use these platforms has not been explored earlier. The sentiment analysis technique was used to examine lenders' sentiments towards the use of P2P lending platforms. The research results show that, ~ 70 percent of lenders showed motivation to use P2P lending platforms as an investment avenue in the future. As the P2P lending platforms are relatively new more research can be carried out in future. 2024 IEEE. -
Sentiment Analysis of Online Hotel Reviews Employing Bidirectional GRU with Attention Mechanism
Online hotel reviews are a more reliable resource for potential hotel guests. Sentiment analysis is a branch of text mining, Natural Processing Language that seeks to identify personality traits, emotions, and other factors. Deep Learning algorithms such as LSTM and GRU have successfully generated context information in sequence learning. However, deep learning cannot focus on the words that contribute the most and cannot capture important content information. This research aims to overcome the inability of LSTM and GRU to capture information. The results are satisfactory, with 93.12% accuracy, 95% ROCAUC, and 95.28% precision recall. This research paper helps managers identify areas to improve their products and services, target marketing campaigns, and identify customer churn. 2024 IEEE. -
Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentiment Analysis on Amazon Product Review
Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today's age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches. 2022 IEEE. -
Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Sentiment Analysis on Educational Tweets: A Case of National Education Policy 2020
Due to COVID-19 pandemic lockdowns, the transition from traditional class-room-based approaches, there has been rise in online education. There is a growing need to adopt the best global academic and innovative practices and implement the National Education Policy-2020 (NEP) in Indian education. This study uses a dataset, NEPEduset, created by gathering tweets about education. An attempt has been made in this study to examine the tweets by preprocessing, generating labels or sentiments using standard tools and libraries in Python language, applying and comparing various machine learning (ML) algorithms. ML approaches are powerful and used in various applications ranging from sentiment analysis, text analysis, natural language processing (NLP), image processing, object detection. ML methods are widely used in sentiment analysis tasks and text annotations. This work uses Text-Blob, Valence Aware Dictionary for Sentiment Reasoning (VADER), and a Customized method, SentiNEP to analyze the sentiment score of tweets' text. SentiNEP method is shown is produce better results for various experiments conducted for the dataset, NEPEduset. Various supervised ML models have been applied for text classification of user sentiment. Word2Vec feature extraction technique has been applied to build and evaluate the models. Performance metrics such as precision, accuracy, F1 score and recall have been used to evaluate the ML models. The results reveal that the support vector machine and random forest classifiers achieve higher accuracy with Word2Vec. The performance results have been compared with VADER, TextBlob and SentiNEP. It has been found that the SentiNEP method produces better results. 2023 IEEE. -
Sentiment Analysis on Indian Government Schemes Using Twitter data
People use social media for entertainment, fetching information, news, business, communication and many more. Few of such social media applications are Facebook, Twitter, WhatsApp, Snapchat and so on. Twitter is one among the micro blogging websites. We are using Twitter mainly because it has gained a lot of media attention. The text written is referred to as tweets, where a common man can tweet or can write their hearts out. We would be fetching the direct responses from the public and hence the data is more real-time. First step is to fetch the tweets on a particular scheme using python language code followed by the cleaning process then comes the creation of bag of words. Later these bags of words are given as an input to the algorithms. Finally, after training the algorithms, we will be getting the sentiment of the public on that scheme. 2019 IEEE. -
Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer
After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE. -
Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Sentimental Analysis on Online Education Using Machine Learning Models
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students feelings. Students favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.