Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Channel Selection; Cognitive radio; Improved genetic algorithm; Spectrum assignment; Spectrum Sensing
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII.
Velmurugan S.; Ezhumalai P.; Mary Anita E.A.
KSII Transactions on Internet and Information Systems, Vol-17, No. 7, pp. 1951-1975.
Velmurugan S., Department of Information Technology, R.M.D. Engineering College, Chennai, India; Ezhumalai P., Department of Computer Science, Engg. R.M.D. Engineering College, Chennai, India; Mary Anita E.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India
Artificial Neural Networks for Enhancing E-commerce: A Study on Improving Personalization, Recommendation, and Customer Experience
AI-powered chatbots; Artificial intelligence; Artificial neural network (ANN); E-commerce; Feature selection; Machine learning
With e-commerce companies, artificial intelligence (AI) has emerged as a crucial innovation that allows companies to streamline processes, improve customer interactions, and increase operational capabilities. To provide tailored suggestions, address client care requests, and improve inventory control, AI systems may evaluate consumer data. Moreover, AI can improve pricing methods and identify fraudulent activity. Companies can actually compete and provide better consumer interactions with the growing usage of machine learning in e-commerce. This essay examines how AI is reshaping the e-commerce sector and creating fresh chances for companies to enhance their processes and spur expansion. AI technology which enables companies to enhance their procedures and offer a more individualized customer experiences has grown into a crucial component of the e-commerce sector. Purpose of providing product suggestions and improve pricing tactics, intelligent machines may examine consumer behavior, interests, and purchase history. Customer service employees will have less work to do as a result of chatbots powered by artificial intelligence handling client queries and grievances. AI may also aid online retailers in streamlining their inventory control by anticipating demands and avoiding overstocking. The use of AI technologies can also identify suspicious transactions and stop economic losses. AI is positioned to assume a greater part in the expansion and accomplishment of the e-commerce sector as it grows in popularity. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Upreti K.; Gangwar D.; Vats P.; Bhardwaj R.; Khatri V.; Gautam V.
Lecture Notes in Electrical Engineering, Vol-1115, pp. 141-153.
Springer Science and Business Media Deutschland GmbH
Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Gangwar D., Department of Management, ADGITM, Delhi, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Bhardwaj R., Chitkara Business School, Chitkara University, Punjab, Rajpura, India; Khatri V., Bhagwan Parshuram Institute of Technology, Delhi, India; Gautam V., Warwick Manufacturing Group, Coventry, United Kingdom
Artistic Representation of Gender Nonconforming Female Bodies in Social Media: A Study of Select Indian Graphic Artists on Instagram
The study critically examines gender nonconforming female identities via their sex-ualized representations through artistic imagination on Instagram. Instagram representation becomes a political act where this visual subversion allows the queer to reclaim their non-binary identity and thus articulate their choices through their body. The digital graphic art taken under study is select images from the Instagram pages of Indian artists artwhoring, aorists, and sayartic. The research study examines the question of an ideal hegemonic femininity perpetuated by the rhetoric of Indian heteronormative patriarchal assertions. It analyses select images that defy hegemonic femininity and gender binary by embodying an amalgamation of masculinity and femininity and lesbian desire which forms an act of subversion. The methodology of critical discourse analysis is employed to study Instagram art and the critical frameworks of the fantasy female body, and the notion of hetero-patriarchal femininity. In conclusion, the study discusses the treatment of female gender non-conforming bodies, their appearance, lesbian desire, and body image. Such transgressive depiction of bodies successfully situates the female body beyond the dichotomy of masculinity and femininity. 2023 The Author(s). All rights reserved.
https://doi.org/10.14321/qed.10.2.0070 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190779636&doi=10.14321%2fqed.10.2.0070&partnerID=40&md5=261da805202862e9d8c8964f6ccaec01
Bose A., Department of English and Cultural Studies, CHRIST University, India; Tanupriya, Department of English and Cultural Studies, CHRIST (Deemed to be University), Delhi NCR campus, India; Singh A., Department of English and Cultural Studies, CHRIST (Deemed to be University), Delhi NCR campus, India
Artists' moving image: South Asian trajectories /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.191-201, ISSN No: 2045-6298.
https://intellectdiscover.com/content/journals/10.1386/miraj.7.2.191_2
Arts education, academic achievement and cognitive ability
Although art is often considered to be a means for maximizing human potential, the causes and consequences of artistic experiences are poorly understood. The present chapter reviews the relevant literature concerning the consequences of participating in the arts. It is clear that training in the arts improves performance on arts-specific tasks. For example, children who take music lessons perform better than their untrained peers on musical tasks such as perceiving musical key and harmony (Corrigall and Trainor, 2009). But training in the arts may also be associated with performance in non-arts domains. This chapter examines the possibility of four such associations, namely whether arts education is associated with academic achievement, general cognitive ability, language processing and visuospatial skills. In each case, the literature is evaluated in terms of the consistency of the findings and the evidence for claims of causation. Training in the arts and academic achievement Training in the arts is associated positively with academic achievement. For example, in a sample of Canadian high-school students, participation in musical activities in the eleventh grade predicted academic achievement in the twelfth grade (Gouzouasis, Guhn and Kishor, 2007). Other results point to similar associations between academic achievement and involvement in any type of arts-related activity. In one study that included more than 25,000 American high-school students, arts participation and school grades were recorded during the eighth, tenth and twelfth grades (Catterall, Chapleau and Iwanaga, 1999). At each point in time, students who were involved in the arts had better grades than other students. A similar positive association emerged in a meta-analysis of five correlational studies (Winner and Cooper, 2000). In a larger meta-analysis of 10 years of data from the American College Board (198898), Vaughn and Winner (2000) concluded that compared to students without arts training, students reporting any form of arts involvement (dance, drama, music and visual arts) obtained higher scores on the Scholastic Aptitude Test (SAT). This advantage for the arts group was evident for the verbal score, the mathematics score and the composite score. Students with drama lessons showed the strongest association, followed (in descending order) by students studying music, painting and dance. Even enrollment in theoretical classes (e.g., music or art history courses) was predictive of better SAT scores. Cambridge University Press 2014.
The Cambridge Handbook of the Psychology of Aesthetics and the Arts, pp. 364-384.
https://doi.org/10.1017/CBO9781139207058.018 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953875736&doi=10.1017%2fCBO9781139207058.018&partnerID=40&md5=934f10fa511de3e24ac4b8a83f51ffee
Swaminathan S., Christ University, Bangalore, India; Schellenberg E.G., University of Toronto, Mississauga, ON, Canada
AS-CL IDS: anomaly and signature-based CNN-LSTM intrusion detection system for Internet of Things
Artificial intelligence; Deep learning; Hybrid intrusion detection; Internet of things; Intrusion detection systems; IoT dataset; Machine learning
In recent years, the internet of things (IoT) has had a significant impact on our daily lives, offering various advantages for improving our quality of life. However, it is crucial to prioritize the security of IoT devices and the protection of user's personal data. Intrusion detection systems (IDS) play a critical role in maintaining data privacy and security. An IoT IDS continuously monitors network activity and identifies potential security risks or attacks targeting IoT devices. While traditional IDS solutions exist, intrusion detection heavily relies on artificial intelligence (AI). AI can greatly enhance the capabilities of IoT IDS through real-time monitoring, precise threat identification, and automatic response capabilities. It is essential to develop and utilize these technologies securely and responsibly to mitigate potential risks and safeguard user privacy. A hybrid IDS was proposed for anomaly-based and signature-based intrusions, leveraging convolutional neural network with long short-term memory (CNN-LSTM). The name of the proposed hybrid model is anomaly and signaturebased CNN-LSTM intrusion detection system (AS-CL IDS). The AS-CL IDS concentrated on two different IoT IDS detection strategies employing a combination of deep learning techniques. The model includes model training and testing as well as data preprocessing. The CIC-IDS 2018, IoT network intrusion dataset, MQTT-IoT-IDS2020, and BoTNeTIoTL01 datasets were used to train and test the AS-CL IDS. The overall performance of the proposed model was assessed using accepted assessment metrics. Despite reducing the number of characteristics, the model achieved 99.81% accuracy. Furthermore, a comparison was made between the proposed model and existing alternative models to demonstrate its productivity. As a result, the proposed model proves valuable for predicting IoT attacks. Looking ahead, the deployment strategy of the IoT IDS can anticipate the utilization of real-time datasets for future implementations. 2023 Jinsi Jose and Deepa V. Jose.
International Journal of Advanced Technology and Engineering Exploration, Vol-10, No. 109, pp. 1622-1639.
https://doi.org/10.19101/IJATEE.2022.10100187 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181473105&doi=10.19101%2fIJATEE.2022.10100187&partnerID=40&md5=0b6c832fb08086e156f2625d5b67c442
Jose J., Department of Computer Science, CHRIST University, Bangalore, India, Department of Data Science, St. Josephs College, Moolamattom, India; Jose D.V., Department of Computer Science, CHRIST University, Bangalore, India
Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model
aspect sentiment extraction; dl based sentiment classification; NLP; Sentiment analysis; text processing
In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models. 2024 River Publishers.
Gupta S.; Singhal N.; Hundekari S.; Upreti K.; Gautam A.; Kumar P.; Verma R.
Journal of Mobile Multimedia, Vol-20, No. 4, pp. 935-960.
https://doi.org/10.13052/jmm1550-4646.2048 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206458908&doi=10.13052%2fjmm1550-4646.2048&partnerID=40&md5=3f3a6567420f45e0ab13abfc3d46cf17
Gupta S., Shobhit Institute of Engineering & Technology, Deemed to-be University, Meerut, 250110, India; Singhal N., Sir Chhotu Ram Institute of Engineering & Technology, Chaudhary Charan Singh University, Meerut, India; Hundekari S., School of Engineering and Technology, Pimpri Chinchwad University, Maval Talegaon, Pune, India; Upreti K., CHRIST University, Delhi NCR Campus, Ghaziabad, India; Gautam A., Manav Rachna International Institute of Research & Studies, Haryana, Faridabad, India; Kumar P., JSS Academy of Technical Education, Noida, India; Verma R., CHRIST University, Delhi NCR Campus, Ghaziabad, India
Aspect Based Multi Classification for Text Mining Using Neural Attention Model
Aspect-based text classification is crucial for multi-classification in e- commerce, including diverse sectors like food, online shopping, and restaurants. Traditional research often focuses on a few classes and domains, such as restaurants or electronics, and overlooks the need to categorize sentences based on domain- specific contexts. However, e-commerce involves numerous domains that require more sophisticated classification methods. E-commerce platforms generate vast amounts of textual data, including comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Since customers often research product reviews from multiple sources before purchasing, these reviews become essential user-generated content for e-commerce businesses. To address this gap, the Aspect-Based Neural Attention Model (ABNAM) was developed. ABNAM enhances classification's accuracy and comprehensiveness by considering each domain's unique characteristics. This leads to better categorization and provides more relevant insights for businesses operating across various e- commerce sectors. Experimental real-world data results demonstrate that ABNAM identifies more meaningful and coherent features. It significantly outperforms other methods by achieving higher accuracy, better recall and precision, and more robust performance across different datasets. The current research introduces an efficient and innovative sentence classification model using ABNAM. Unlike traditional automated text classification models, which struggle to categorize data into sixteen classes, ABNAM excels by leveraging technologies such as TF-IDF, N-Gram, Convolutional Neural Networks (CNN), Linear Support Vector Machines (SVM), Random Forest, and Nae Bayes. Among these methods, ABNAM achieved the highest accuracy at 97%, successfully classifying sentences into one of the sixteen categories. The research positions ABNAM as a novel and highly effective classification model, particularly in achieving high-class categorizations.
Aspect based sentiment analysis using a novel ensemble deep network
Aspect-based sentiment analysis; Ensemble deep network; Graph neural network; Sentiment analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1-score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model. 2024, Institute of Advanced Engineering and Science. All rights reserved.
IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 1668-1678.
Institute of Advanced Engineering and Science
All Open Access; Hybrid Gold Open Access
https://doi.org/10.11591/ijai.v13.i2.pp1668-1678 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193721372&doi=10.11591%2fijai.v13.i2.pp1668-1678&partnerID=40&md5=9b65ac301a0655a258a223fa4e4f46f0
Rajan A., Department of Computer Science and Engineering, Christ University, Bangalore, India; Manur M., Department of Computer Science and Engineering, Christ University, Bangalore, India
Aspect based sentiment analysis using fine-tuned BERT model with deep context features
BERT; DC-BERT; Interaction layer; NLP; Sentiment analysis
Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively. 2024, Institute of Advanced Engineering and Science. All rights reserved.
IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 1250-1261.
https://doi.org/10.11591/ijai.v13.i2.pp1250-1261 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192977653&doi=10.11591%2fijai.v13.i2.pp1250-1261&partnerID=40&md5=8dbd977ad8d0931bcfe536fed951a8ab
Rajan A., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Manur M., Department of CSE, CHRIST (Deemed to be University), Bangalore, India
Assault on religion from modern behavioural and natural sciences
Journal of Dharma, Vol-30, No. 2, pp. 157-183.
Thadavanal J., Department of Psychology, Christ College, Dharmaram Vidya Kshetram, Bangalore, India
Assembly of discrete and oligomeric structures of organotin double-decker silsesquioxanes: Inherent stability studies
Double-decker silsesquioxane (DDSQ), a type of incompletely condensed silsesquioxane, has been used as a molecular precursor for synthesizing new organotin discrete and oligomeric compounds. The equimolar reaction between DDSQ tetrasilanol (DDSQ-4OH) and Ph2SnCl2 in the presence of triethylamine leads to obtaining discrete [Ph4Sn2O4(DDSQ)(THF)2] (1). The change of sterically bulky aryl Ph2SnCl2 precursor to linear alkyl nBu2SnCl2 led to the isolation of oligomeric [nBu4Sn2O4(DDSQ)] (2). The structures of compounds 1 and 2 have been demonstrated using single-crystal X-ray diffraction measurements. Indeed, the formation of oligomeric organotin DDSQ compound (2) was determined using GPC and MALDI-TOF mass spectroscopy. In compound 1, the geometry of the tin atom is five-coordinated trigonal bipyramidal by two phenyl groups, two Si-O from DDSQ and one tetrahydrofuran. Compound 2 contains four coordinated two peripheral tin atoms and two five-coordinated central tin atoms, in which, the fifth coordinating oxo groups in the central tin atoms create the bridge between two different DDSQ units that leads to the formation of oligomeric structure. Density functional theory calculations on organotin DDSQs infer that the obtained lattice energy for compound 1 is far higher than for the case of compound 2, which indicates that the crystal of compound 1 is better stabilized in its crystal lattice with stronger close packing via intermolecular interactions between discrete molecules with coordinated THF compared to the crystal of compound 2. The greater stability arises mainly due to the sterically bulkier phenyl groups attached to the tin centers in compound 1, which provide accessibility for accommodating the THF molecule per tin via Sn-THF bonding, while contrarily the smaller n-butyl groups aid the polymerization of the four repeating units of [SnSi4O7] or two Sn2O4(DDSQ) through ?-oxo groups. Both compounds 1 and 2 were chosen to be promising precursors for the synthesis of ceramic tin silicates. The thermolysis of 2 at 1000 C afforded the mixture of crystalline SnSiO4 and SiO2 but the same mixture was only formed by thermolysis of 1 at relatively higher temperature (1500 C), which infers that compound 1 is more stable than compound 2 that is in good synergy with theoretical lattice energy. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique.
Loganathan P.; Pillai R.S.; Jeevananthan V.; David E.; Palanisami N.; Bhuvanesh N.S.P.; Shanmugan S.
New Journal of Chemistry, Vol-45, No. 43, pp. 20144-20154.
https://doi.org/10.1039/d1nj03128g https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119263516&doi=10.1039%2fd1nj03128g&partnerID=40&md5=3a7e18ac4be8c6528e07ead8dd79c281
Loganathan P., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Pillai R.S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India, Department of Chemistry, Christ University, Karnataka, Bangalore, 56029, India; Jeevananthan V., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; David E., Centre for Functional Materials, Department of Chemistry, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Palanisami N., Centre for Functional Materials, Department of Chemistry, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Bhuvanesh N.S.P., X-ray Diffraction Laboratory, Department of Chemistry, Texas AandM University, College Station, 77842, TX, United States; Shanmugan S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India
Assesment of bone mineral density in X-ray images using image processing
Bone densitometry; Bone mineral Density; DEXA; Osteoporosis
X-ray application in medical fields has given rise to various research challenges related to bone, due to its wide usage in finding out the disease related to human anatomy. It has lot of research challenges to solve using available wide application of medical imaging techniques and inspired by this, a novel X-ray images based survey was conducted to understand the role of Xray images in medical field. Bone mass density identification is the standard procedure to monitor the risk of fracture in bone using DEXA. Lot of research has been carried out to calculate BMD using X-ray images and it provided prominent results. Since Xray is economically affordable and very economical compared to DEXA, we have decided to work on X-ray images. This paper explains us about various current advancements and disadvantages with respect to X-ray image in medical sector and various techniques related to BMD calculation. X-ray images characteristics and its fundamentals in the medical field for identifying bone related diseases are also discussed. 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.
Proceedings of the 2021 8th International Conference on Computing for Sustainable Global Development, INDIACom 2021, pp. 905-908.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/INDIACom51348.2021.00162 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107969876&doi=10.1109%2fINDIACom51348.2021.00162&partnerID=40&md5=271eb2791f264a25924edd63147c8232
Dodamani P.S., Christ University, Department of Computer Science and Engineering, Bangalore, India; Danti A., Christ University, Department of Computer Science and Engineering, Bangalore, India
Assessing Academic Performance Using Ensemble Machine Learning Models
Academic Assessment; Ensemble Model; Forecasting; Machine Learning; Prediction
Artificial Intelligence (AI) shall play a vital role in forecasting and predicting the academic performance of students. Societal factors such as family size, education and occupation of parents, and students' health, along with the details of their behavioral absenteeism are used as independent variables for the analysis. To perform this study, a standardized dataset is used with data instances of 1044 entries and a total of 33 unique variables constituting the feature matrix. Machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), LightGBM, and Ensemble Stacking (ES) are used to assess the specified dataset. Finally, an ES model is developed and used for assessment. Comparatively, the ES model outclassed other ML models with a test accuracy of 99.3%. Apart from accuracy, other parameters of metrics are used to evaluate the performance of the algorithms. 2023 IEEE.
Siva Balan R.V.; Senthilnathan T.; Gobinath R.; Gondkar R.R.
Proceedings of the International Conference on Circuit Power and Computing Technologies, ICCPCT 2023, pp. 917-924.
https://doi.org/10.1109/ICCPCT58313.2023.10245216 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173863491&doi=10.1109%2fICCPCT58313.2023.10245216&partnerID=40&md5=6bff54ba6ce7ab745bfd0818fb1d2254
Siva Balan R.V., CHRIST (Deemed-to-be University), Department of Computer Science, Karnataka, Bangalore, India; Senthilnathan T., CHRIST (Deemed-to-be University), Department of Computer Science, Karnataka, Bangalore, India; Gobinath R., CHRIST (Deemed-to-be University), Department of Computer Science, Karnataka, Bangalore, India; Gondkar R.R., CHRIST (Deemed-to-be University), Department of Computer Science, Karnataka, Bangalore, India
Assessing and Exploring Machine Learning Techniques for Cardiovascular Disease Prediction using Cleveland and Framingham Datasets
cardiovascular disease (CVD); Cleveland Dataset; Decision Tree (DT); Framingham Dataset; HD (HD); Logistic Regression (LR); Machine Learning (ML); Random Forest (RF); Support Vector Machine (SVM)
Heart disease prediction using machine learning has garnered significant attention due to its potential for early diagnosis and intervention. This study presents an analysis of various machine learning algorithms applied to HD prediction across multiple research papers. The goal of this study is to analyze the performance and predictive capabilities of various machine learning algorithms in predicting heart disease across different datasets and research papers. Algorithms such as Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Naive Bayes, and Gradient Boosting were evaluated using diverse datasets and parameters. In the Cleveland dataset, both Random Forest and Decision Tree classifiers achieved perfect accuracy 100%. Conversely, in the Framingham dataset, Random Forest exhibited the highest accuracy at 94%, followed by SVM at 87.45%, and Decision Tree at 85.23%. While specific algorithm performance varies depending on the dataset and parameters considered, ensemble methods like Random Forest often demonstrate superior performance. These findings underscore the effectiveness of machine learning in HD prediction and emphasize the significance of algorithm selection in developing accurate predictive models for cardiovascular health. 2024 IEEE.
2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
Roopini J., Reva University, School of Csa, Bangalore, India; Deepa B.G., Christ University, Dept. of Computer Science, Bangalore, India; Pooja N.G., New Horizon College, Dept. of Cse, Bangalore, India
Assessing anticancer properties of PEGylated platinum nanoparticles on human breast cancer cell lines using in-vitro assays
anticancer property; AO/PI dual staining; DCFH DA ROS assay; MTT assay; platinum nanoparticles; polyethylene glycol
This study describes the in-vitro cytotoxic effects of PEG-400 (Polyethylene glycol-400)-capped platinum nanoparticles (PEGylated Pt NPs) on both normal and cancer cell lines. Structural characterization was carried out using x-ray diffraction and Raman spectroscopy with an average crystallite size 5.7 nm, and morphological assessment using Scanning electron microscopy (SEM) revealed the presence of spherical platinum nanoparticles. The results of energy-dispersive x-ray spectroscopy (EDX) showed a higher percentage fraction of platinum content by weight, along with carbon and oxygen, which are expected from the capping agent, confirming the purity of the platinum sample. The dynamic light scattering experiment revealed an average hydrodynamic diameter of 353.6 nm for the PEGylated Pt NPs. The cytotoxicity profile of PEGylated Pt NPs was assessed on a normal cell line (L929) and a breast cancer cell line (MCF-7) using the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results revealed an IC50 of 79.18 ?g ml?1 on the cancer cell line and non-toxic behaviour on the normal cell line. In the dual staining apoptosis assay, it was observed that the mortality of cells cultured in conjunction with platinum nanoparticles intensified and the proliferative activity of MCF-7 cells gradually diminished over time in correlation with the increasing concentration of the PEGylated Pt NPs sample. The in vitro DCFH-DA assay for oxidative stress assessment in nanoparticle-treated cells unveiled the mechanistic background of the anticancer activity of PEGylated platinum nanoparticles as ROS-assisted mitochondrial dysfunction. 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Biomedical Physics and Engineering Express, Vol-10, No. 6
https://doi.org/10.1088/2057-1976/ad795d https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204819867&doi=10.1088%2f2057-1976%2fad795d&partnerID=40&md5=9bb20fdd8e84ea8f44568762e4f0f380
Aswathy F.A.W., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India; Jose J., Division of Microbiology, Department of Biosciences, Rajagiri College of Social Sciences (Autonomous), Kerala, Cochin, 683104, India; Anila A.E.I., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India
Assessing Climate Change through Artificial Intelligence An Ethico-Legal Study
IPCC (The Intergovernmental Panel on climate change) [1], in the 6th Assessment Report released in 2022, reports that the net anthropogenic GHGs (greenhouse gases) continued to rise during the period 2010-19. It shows that GHG emission in the last decade is the highest in human history. According to the World Inequality Report, 2022, carbon dioxide concentration level in the atmosphere across the globe is the highest in millions of years. Consistent rise in the global emission level leading to alarming rise in atmospheric temperature has been a cause of concern for mankind. Rising atmospheric temperature leading to climate change has severely affected weather patterns; led to melting of glaciers; caused natural disaster and extinction of species, and severely impacted the ground water table. It has put the human race at a crossroads and thrown open an existential question for the world. Attempts have been made, both international and national, to reverse the impact of the rising scenario concerning climate change but have yet to be successful. The technological revolutions arising in recent times, especially in the domain of Artificial Intelligence (AI), offer hope to give a new shape to human civilization. With the aid of human intelligence, AI can perform assessment and predictive work as well which may help in mitigating the effect of adversely affecting climate change and help improve the environment. As per UNESCO (United Nations Educational, Scientific and Cultural Organisation), AI can perform assessment and prediction of climate change, which may assist in the protection of the environment. The Council identifies three priority areas relating to use of AI which includes improved understanding and predictions of climate change and geohazards [2]. This chapter aims at exploring the contribution of AI in assessing the behavioral pattern of climate change and the ethico-legal challenges involved therein. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.
Artificial Intelligence in Forecasting: Tools and Techniques, pp. 130-145.
Mishra A., School of Law, Christ University, Karnataka, Bangalore, India; Pandey A., School of Law, Christ University, Karnataka, Bangalore, India; Mohanty S., School of Law, Christ University, Karnataka, Bangalore, India
Assessing CRM practices in hotel industry: A look at the progress and prospects
CRM; CRM Levels; CRM process; Customers; Hotel industry; Importance level; Performance level
Purpose: Customer Relationship Management (CRM) is a unique key element considered in the hotel diligence which is extremely thriving plus competitive market at present. The study purpose is to identify how CRM can be strategy tactics for solution as business attitudes towards the hotels. It generates nine different items with regards to CRM. Design/methodology/approach: The study progressed in four phases. The empirical data were collected. A sample of 100, four, five and five star luxury hotels was considered for the study. To explore CRM process in hotel industry, an analysis was carried out to identify different CRM levels (performance and importance). The study was tested and analyzed by adopting a scale developed by Reinartz, Krafft and Hoyer. Findings: The result largely supports the CRM process developed in hotels theoretically but does not seem to have the balance between the CRM levels evenly. Crucial discoveries of the learning are firstly, CRM remains characterized via nine different items in the hotel diligence connecting to Measure, Acquisition, Regain, Maintain, Retain, Cross up, Referrals, Termination Measure and Exit. The best predictor of CRM is the items on performance level referred as Measure followed by Regain and Acquisition and on the importance level, Referrals followed by Cross-up and Regain. Practical Implications: The result suggests that the hotels must understand today's customer's wants and needs. The hotels need to be more proactive in developing a balance between CRM levels for effective CRM in hotel diligence. Novelty/assessment: The learning highlights the importance of CRM and its relationship in service industries in the literature. The study focuses in particular on CRM in hotel industry and the linkage between the CRM levels (Performance and Importance).
Indian Journal of Science and Technology, Vol-8, pp. 82-90.
Indian Society for Education and Environment
https://doi.org/10.17485/ijst/2015/v8iS6/62088 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928813003&doi=10.17485%2fijst%2f2015%2fv8iS6%2f62088&partnerID=40&md5=d6ca78050af21250cb4bb456a583de6a
Fukey Leena N., Department of Hotel Management, Christ University, Bangalore, 29, India; Jaykumar V., Department of Hotel Management, Christ University, Bangalore, 29, India; Issac S.S., Department of Hotel Management, Christ University, Bangalore, 29, India
Assessing factors influencing intentions to use cryptocurrency payments in the hospitality sector
Behavioral reasoning theory; Cryptocurrency payment; Hospitality; PLS-SEM; Use intention
Purpose: The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry. Design/methodology/approach: The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1,080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Findings: The outcome of this work showed that the reasons for positively influence and reasons against negatively influence consumers attitudes and use intentions. Consumers values of openness to change positively influence the reasons for and do not influence the reasons against and attitude toward the use of cryptocurrency payments. Practical implications: This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government. Originality/value: This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector. 2024, Emerald Publishing Limited.
Pillai R.; Preet R.; Sivathanu B.; Rana N.P.
https://doi.org/10.1108/ITP-07-2023-0673 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200560638&doi=10.1108%2fITP-07-2023-0673&partnerID=40&md5=b8912d6b759bd0b812b68d800c4171e0
Pillai R., Department of Management, Pune Institute of Business Management, Pune, India; Preet R., Department of Management, Pune Institute of Business Management, Pune, India; Sivathanu B., School of Business and Management, Christ University, Bangalore, India; Rana N.P., Queen's Business School, Queen's University Belfast, Belfast, United Kingdom, Chandragupt Institute of Management Patna, Patna, India
Purpose The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry. Design/methodology/approach The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1, 080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Findings The outcome of this work showed that the reasons for positively influence and reasons against negatively influence consumers attitudes and use intentions. Consumers values of openness to change positively influence the reasons for and do not influence the reasons against and attitude toward the use of cryptocurrency payments. Practical implications This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government. Originality/value This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector. 2024 Emerald Publishing Limited
Pillai, Rajasshrie; Preet, Raman; Sivathanu, Brijesh; Rana, Nripendra P.
Information Technology and People;Volume;38;Issue;6;pp.2477-2505
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1108/ITP-07-2023-0673 https://www.scopus.com/pages/publications/85200560638?origin=resultslist
Pillai R., Department of Management, Pune Institute of Business Management, Pune, India; Preet R., Department of Management, Pune Institute of Business Management, Pune, India; Sivathanu B., School of Business and Management, Christ University, Bangalore, India; Rana N.P., Queen's Business School, Queen's University Belfast, Belfast, United Kingdom