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Smart Home Activity Recognition for Ambient Assisted Living (AAL)
With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer's Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual's well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. In this research paper, a smart home activity recognition model is proposed and implemented using four machine learning algorithms using six different publicly available datasets. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the dataset. The Electrochemical Society -
Cataloging of happy facial affect using a radial basis function neural network
The paper entitled "Cataloging of Happy facial Affect using a Radial Basis Function Neural Network" has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human-computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results. 2013 Springer. -
An Integrated and Optimized Fog Computing enabled Framework to minimize Time Complexity in Smart Grids
A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. 2023 IEEE. -
Rectangular Microstrip Patch Antenna Array Based Sectored Antenna for Directional Wireless Sensor Networks
Directional wireless Sensor Network (DSN) outperforms Wireless Sensor Network (WSN) over different parameters such as transmission range, interference, spatial reusability and energy efficiency. In this paper, a Rectangular Patch antenna Array (RPA) based sectored antenna is proposed for DSN. The individual sector is composed of two-element rectangular patch antenna array with a measured peak gain of 5.2 dBi and half-power beamwidth of 45. Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard is designed to connect the sectored antenna to MICAz WSN mote. The antenna performance analysis carried out in simulation and real-time measurement via Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA) exhibits higher gain, lower return loss, half-power beamwidth and Voltage Standing Wave Ratio (VSWR). 2020 IEEE. -
Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Prefabricated Houses - A Model to Sustainable Housing Market
Effective spatial planning in an urban center is the need of the hour especially for offering affordable and sustainable houses. separate sheet. Spatial planning may help in achieving Sustainable Development goals (SDG) (09) Industry, Innovation and Infrastructure, (11) Sustainable cities and communities, and (15) Life on Land. The prefabricated house model can be used as a strategy in achieving above mentioned SDGs. It is important to study the prefabricated housing market for a country like India, considering its growing population and the necessity of access to affordable and sustainable houses. The main objective of this study is to identify the determinant factors of prefabricated houses and its impact on preference among urban consumers. The study is quantitative in nature and adopts a survey method. SEM model is used to analyze the data. A structured questionnaire is developed based on the objectives of the study. The questionnaire majorly focused on the perception of Sustainability, Affordability, Durability, Barriers, Opportunities, and Quality. The Electrochemical Society -
ML-Based Prediction Model for Cardiovascular Disease
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm
The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother's womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Nae Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early. 2024 IEEE. -
Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
Sentiment based stock price direction detection has been an exciting study in the field of finance which is drawing a lot of attention from the investor community. Sentiments are used to detect the changes in the stock price movements for the subsequent periods. Investor community uses these sentiments derived from news, celebrity speech and events to plan trading and investment strategies. Several studies have been done in the past with sentiments, but use of Earnings Call Transcripts (ECT) has not been explored for market intelligence hitherto. Standard dictionary based lexicons like Vader, AFINN and NRC have not performed well in finance as they are domain agnostic. There is a need to develop a financial lexicon based on the ECT corpora, which may provide a better lift over the standard lexicons. This study has observed that Vader has performed poorly as opposed to the newly developed financial lexicon. Machine learning based generative lexicon engine using Bayesian approach, which is termed as FNB Lex was developed in this research study to overcome the limitations of standard domain agnostic lexicons. The lexicon development was performed on quarterly Earning Call Transcripts (ECT) of sixteen IT companies spanning over ten years. The study also investigates the detection of inverse effect in stock price movements based on the sentiments of the previous period. Machine Learning (ML) models like Naive Bayes, FNB Lex, SVM and biLSTM were developed and their discriminatory powers were assessed. NB Lex provided much better lift in detecting the inverse effect as opposed to other models. 2024 IEEE. -
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE. -
Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence
In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE. -
A Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence
Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society -
IoT Cloud Systems: A Survey
IoT has gained a massive prevalence in the last decade. Various businesses are leveraging IoT Applications for industrial and commercial use cases. IoT also presents use cases in research and academia. However, setting up IoT Systems is complex due to the distributed and multi-disciplinary nature of IoT Systems. As a direct consequence of this complexity, the entire service industry has emerged that assists users to deploy and manage IoT systems. This paper aims to survey some of the Cloud management systems that help simplify and shorten the deployment process of IoT Systems. 2023 IEEE. -
Machine Learning based Food Sales Prediction using Random Forest Regression
Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE. -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Content Based Deep Factorization Framework for Scientific Article Recommender System
With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content Based Scientific Article Recommendation System Using Deep Learning Technique
The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Document Classification for Recommender Systems Using Graph Convolutional Networks
Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Similarity Analysis for Citation Recommendation System using Binary Encoded Data
Citations are a crucial part of an academic dissertation, project or scientific work. The most time-consuming task for any scholar is to find suitable citations for any work. Thus, a convenient citation recommendation system provides completeness and fulfillment for citing the giants' works. Moreover, attaining high quality for any citation recommendation system is challenging as it should not only recommend relevant papers but also should match the context of the paper. An advanced algorithm SABED (Similarity Analysis using Binary Encoded Data) has been proposed that converts text metadata of the article like author name, doi of the paper, keywords, abstract and content of the paper into the binary format and is fired into the database. The binary formatted query fired fetches the accurate matches thereby increasing the accuracy of search probability and similarity analysis. This similarity analysis can be further used to provide recommendations to the users. The proposed system concentrates on the similarity of the content and hence the context of the papers is not taken into consideration. 2020 IEEE. -
Ceria doped titania nano particles: Synthesis and photocatalytic activity
Ceria (0.5, 1 and 2 mol%) doped titania nano catalysts were prepared by combustion synthesis method, using titanium isopropoxide as the starting material. The prepared catalysts were characterized by X-ray diffraction (XRD), Energy dispersive X-ray analysis (EDX), Scanning electron microscopy (SEM) and Infra red spectroscopy (FTIR). Total acidity of the prepared catalysts were determined by temperature programmed desorption of ammonia (TPD - NH3). XRD pattern of 1% ceria doped titania obtained by calcinations at 873 K indicated that the samples were crystalline with a mixture of anatase and rutile phase. No peaks corresponding to cerium oxide were observed XRD patterns indicating that the amount of cerium is negligible on the surface of titania catalyst. The photo catalytic activity was evaluated for the degradation of methylene blue (MB) under visible light irradiation. The degradation rates of MB on cerium doped TiO2 samples were higher than that of pure TiO2. The introduction of structural defects (cationic ceria dopant) into the titania crystal lattice leads to the change of band gap energy. As a result, the excitation energy is expanded from UV light of anatase TiO2 to visible light for ceria doped titania. 2016 Elsevier Ltd.