Smart Precision Irrigation Techniques Using Wireless Underground Sensors in Wireless Sensors
IoT; Irrigation; Smart Precision Agriculture; Weather Prediction; Wireless Sensors
The term brilliant accuracy agribusiness alludes to advances like the Internet of Things, remote sensors, and artificial reasoning on the ranch. A definitive objective of this paper is to expand the quality and amount of the harvests while advancing the human work utilized, use of water system, and diminishing the water system times in light of climate forecast. This proposed framework utilizes the three kinds of sensors like Moisture sensor is used to detect the dirt dampness, the Humidity sensor is utilized to return how much water is present in the encompassing air, Temperature sensors are utilized to give the temperature of the dirt. Each of the three qualities is passed to the remote sensor hub and moved to the information lumberjacks. At long last, the information lumberjack will send the message like beginning the water system to a programmed water lock framework. When all water measures arrive at the water system limit, the programmed water lock framework will be shut. By using the underground, remote sensors, we can track the conditions of various agricultural applications such as soil properties, seed varieties and monitor the environmental situations. Every gadget contains significant gear like sensors, memory, a processor, and a power source. 2022 IEEE.
Sathiyapriya K.; Thaiyalnayaki S.; Sambandam R.K.; Anuranjani K.; Vetriveeran D.
8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, pp. 2072-2077.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACCS54159.2022.9785190 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133173513&doi=10.1109%2fICACCS54159.2022.9785190&partnerID=40&md5=db658dabd5f336785d338dd83a7e0045
Sathiyapriya K., School of Computing, Bharath Institute of Higher Education and Research (Deemed to Be University), Dept. of Computer Science and Engineering, Tamilnadu, Chennai, India; Thaiyalnayaki S., School of Engineering and Technology, Christ (Deemed to Be University, Dept. of Computer Science and Engineering, Karnataka, Bangalore, India; Sambandam R.K., School of Engineering and Technology, Christ (Deemed to Be University, Dept. of Computer Science and Engineering, Karnataka, Bangalore, India; Anuranjani K., School of Computing, Bharath Institute of Higher Education and Research (Deemed to Be University), Dept. of Computer Science and Engineering, Tamilnadu, Chennai, India; Vetriveeran D., School of Engineering and Technology, Christ (Deemed to Be University, Dept. of Computer Science and Engineering, Karnataka, Bangalore, India
AI and Machine Learning Enabled Software Defined Networks
Artificial intelligence; Distributed denial of service and software-defined networking; Machine learning
The telecommunications industry has not been exempt from the technology sectors massive artificial intelligence (AI) and machine learning (ML) boom in recent years. Artificial intelligence (AI) and machine learning (ML) provide advanced analytics and automation that are in line with modern networking concepts like software-defined networking (SDN) and software-defined wide-area networks (SD-WAN). Work is being done to determine how AI/ML can benefit SD-WAN and to demonstrate these benefits in a real SD-WAN network using a workable example. Modern ML techniques and algorithms are the extent of AI/ML. Todays Internet is under constant threat from DDoS (Distributed Denial of Service) attacks. As the volume of Internet traffic grows, its getting harder and harder to tell whats legitimate and whats malicious. The DDoS attack was detected using a machine learning approach that makes use of a Random Forest classifier. To better detect DDoS attacks, we tweak the Random Forest algorithm. The proposed machine learning approach outperforms, as demonstrated by our results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Satheesh K.K.S.V.A.; Janani M.; Venkateswarlu S.C.; Kumar R.G.; Gupta A.; Kotaiah B.
Lecture Notes in Networks and Systems, Vol-446, pp. 131-144.
Springer Science and Business Media Deutschland GmbH
Satheesh K.K.S.V.A., School of Computer Science and Artificial Intelligence, SR University, Hanuma Konda, Warangal, India; Janani M., IT Department, St. Josephs College of Engineering, Hyderabad, India; Venkateswarlu S.C., Electronics and Communication Engineering, Institute of Aeronautical Engineering, Telangana, Dundigal, Hyderabad, India; Kumar R.G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Gupta A., Amity Business School, Amity University, Uttar Pradesh, Greater Noida, India; Kotaiah B., Department of CS and IT, Maulana Azad National Urdu Central University, Telangana, Gachibowli, Hyderabad, India
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society
Anand G.; Srivastava S.; Shandilya A.; Gupta V.
ECS Transactions, Vol-107, No. 1, pp. 19991-20003.
https://doi.org/10.1149/10701.19991ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134030031&doi=10.1149%2f10701.19991ecst&partnerID=40&md5=ee7d282a8bedeb573f069fc62248a25d
Anand G., School of Sciences, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, 201003, India; Srivastava S., School of Sciences, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, 201003, India; Shandilya A., Department of Computer Science, Geeta Engineering College, Haryana, Panipat, 132145, India; Gupta V., School of Sciences, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, 201003, India
Significance of extra-framework monovalent and divalent cation motion upon CO2 and N2 sorption in zeolite X
Adsorption; Divalent cations; Flue gas; GCMC (Grand Canonical Monte Carlo); Zeolite-X
Experimental observations and the GCMC (Grand Canonical Monte Carlo) simulations with fixed and mobile cations in their cavities have been used to study nitrogen and carbon dioxide sorption in divalent cation (Ca, Sr, and Ba) exchanged Zeolite X. Simulations of carbon dioxide and nitrogen adsorption isotherms and the heat of adsorption in divalent cation exchanged zeolite X produced results that were similar to those found in experimental results. Both experimental and simulated isotherms showed that carbon dioxide adsorption capacity is saturated at lower pressure with high adsorption capacity than the nitrogen isotherm in all zeolite samples. In the order of electronegativity of the extra-framework cations, the isosteric heat of sorption results show that carbon dioxide as well as nitrogen molecules interact more with divalent metal ion exchanged zeolites. Simulations of carbon dioxide and the nitrogen sorption in zeolite -X revealed that the mobile extra-framework cations in the cages of zeolite X had a significant advantage over zeolite molecular sieves in the separation process. The simulation with mobile cations can be a good tool for developing selective and purposeful zeolite-based adsorbents by knowing the cation position and its migration upon the adsorption of various gases. 2022
Materials Today: Proceedings, Vol-68, pp. 85-92.
https://doi.org/10.1016/j.matpr.2022.06.105 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132932653&doi=10.1016%2fj.matpr.2022.06.105&partnerID=40&md5=f572af11c7591066db2c989b75f98070
Peters S., Department of Chemistry, School of Basic Sciences, SRM Institute of Science and Technology, Kattankulathur, 603 203, India; Pillai R.S., Department of Chemistry, Christ University, Bangalore, 560029, India; Jasra R.V., Reliance Technology Group, Reliance Industries Limited, Vadodara Manufacturing Division, Vadodara, 391 346, India
P4 based Load Balancing Strategies for Large Scale Software-Defined Networks
DPDK; Load Balancing mechanisms; P4 Language; SHELL; Software-Defined Network
To meet the large demands of future networks, several large-scale Software Defined Networking (SDN) test-beds have been designed. The increasing complexity of networks has resulted in convoluted methods for managing and orchestrating efficiently across a wide range of network environments. The load balance function is impaired when the controller fails to connect with the switches. A traditional Load Balancer (LB) must decapsulate layers one by one and get the information needed to run load balancing algorithms. For instance, OpenFlow, NetConf, Programming Protocol-independent Packet Processors (P4), and Data Plane Developement Kit (DPDK) provide network programmability at both the control and data plane levels. In this paper, authors implement load balancing using the P4 programming language without the need of a controller, the P4 load balancer can operate on its own. Controller's support is used to keep track on the health of the web servers. In this situation, the controller can identify a server failure and notify the P4 load balancer, which will restrict requests to the malfunctioning server, lowering the dispatching failure rate. A detailed investigation of various load balancing mechanisms is analysed in this paper followed by the identification of four potential approaches to large-scale SDN tests, including connection hash, weighted round-robin, DPDK technique, a Stateless Application-Aware Load-Balancer (SHELL). 2022 IEEE.
2022 2nd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2022
Kulkarni M., Christ University, Department of Computer Science, Bangalore, India; Goswami B., School of Computer Science Queensland, University of Technology, Brisbane, Australia; Paulose J., Christ University, Department of Computer Science, Bangalore, India
Structural and morphological characterization of hydrothermally synthesized N-Carbon Dot @ Fe3O4 composites for heavy metal ion detection
Carbon Dots; Composites; Fluorescence; Heavy metal; Hydrothermal
Heavy Metal-ion contamination is one of the most serious issues facing day-to-day life. To address this issue, sensing and removal of heavy metal ions in contaminated water become indispensable. Carbon Dots are hydrophilic in nature with magnificent electron acceptor and electron donator and hence it has been used as fluorescent probes for sensing applications. The present study deals with the synthesis of N-Carbon Dot (N-CD) @ Fe3O4 composite which was successfully fabricated via the hydrothermal method. The surface structure and morphology of the synthesized composite were characterized using X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). The elemental analysis of a sample was characterized using Energy Dispersive Spectroscopy (EDS). Further, the phase occurrence and the molecular vibration were analysed using XRD and Fourier Transform Infra-Red Spectroscopy (FTIR). Finally, the optical studies were measured using Ultravioletvisible Spectroscopy (UV Vis) and Photoluminescence Spectroscopy (PL). The prepared composite exhibited noticeable fluorescence properties and has promising potential for the detection and removal of toxic heavy metal ions in water. 2022
Abinaya S.; Baby A.; Gurunathan K.; Sreeja P.B.; Jose S.P.
Materials Today: Proceedings, Vol-64, pp. 1854-1858.
https://doi.org/10.1016/j.matpr.2022.06.321 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133404289&doi=10.1016%2fj.matpr.2022.06.321&partnerID=40&md5=b4d3caf410ae045d2a3fc97e9ab33f16
Abinaya S., Advanced Materials Laboratory, School of Physics, Madurai Kamaraj University, Madurai, 625021, India; Baby A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 56002, India; Gurunathan K., Advanced Materials Laboratory, School of Physics, Madurai Kamaraj University, Madurai, 625021, India; Sreeja P.B., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 56002, India; Jose S.P., Advanced Materials Laboratory, School of Physics, Madurai Kamaraj University, Madurai, 625021, India
Interval-valued fuzzy tree (IVFT) and interval-valued fuzzy cycle (IVFC) are defined in this chapter. We characterize interval-valued fuzzy trees. We also prove that if G is an IVFG whose underlying crisp graph is not a tree then G is an IVFT if and only if G contains only ? strong arcs and weak arcs. It is shown that an IVFG G whose underlying crisp graph is a cycle is an IVFC if and only if G has at least two ? strong arcs. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Philip A.M.; Kalayathankal S.J.; Kureethara J.V.
Lecture Notes in Networks and Systems, Vol-462, pp. 251-263.
Philip A.M., Assumption College, Changanacherry, India; Kalayathankal S.J., Jyothi Engineering College, Thrissur, India; Kureethara J.V., Christ University, Bangalore, India
An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
Convolution Neural Network; Deep Learning; Disaster Tweet; Gated Recurrent Unit; Late Fusion; VGG-16
Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE.
Mondal A.; Kesan A.; Rodrigues A.; George J.
IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022
https://doi.org/10.1109/ICDCECE53908.2022.9792951 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133428851&doi=10.1109%2fICDCECE53908.2022.9792951&partnerID=40&md5=688a26df6821cad4e8d36801c58adb26
Mondal A., Deemed to Be University, Department of Data Science Christ, India; Kesan A., Deemed to Be University, Department of Data Science Christ, India; Rodrigues A., Deemed to Be University, Department of Data Science Christ, India; George J., Deemed to Be University, Department of Data Science Christ, India
Secure Authenticated Communication Via Digital Signature And Clear List In VANETs
Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system(ITS), When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication,a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without CRL list, but mentions a key management list to overcome large storage space and checking time even it reduce the access delay too. For the access permission we can do an authentication system based digital novel signature authentication(DNSA) for each vehicles in the vanet with the RSU unit or with other participant node vehicles in the communication as per the Topology.So we can perform an efficient and secured communication in VANET. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 20065-20071.
https://doi.org/10.1149/10701.20065ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134001069&doi=10.1149%2f10701.20065ecst&partnerID=40&md5=de363854ae31480c5d5282f2b286d3d3
Deljo D., Department of Computer Science and Engineering, Christ university, Banglore, 560029, India; Kumar K.P., Department of Computer Science and Engineering, Christ university, Banglore, 560029, India
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
CBIR; CBMIR; Euclidean Distance Method; LBP; Precision; Recall
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE.
Ramamurthy B., CHRIST(Deemed to Be University), Computer Science Department, Karnataka, Bangalore, India; Prasanna M., SITE, Vit, Tamil Nadu, Vellore, India
Online Education and English Language Learning Among Tribal Students of Kerala
Kerala, a South Indian state has tribal population in all her districts. About 1.5% of the total population of the state constitute tribal population. They depend upon natural environment and resources for their survival. Children from the same community usually depend on government funded schools for their education. Education for this deprived section during COVID 19 Pandemic was a massive exclusion and an uphill task. Digital divide and medium of communication (Standard Malayalam) were some of the critical concerns to knowledge acquisition among tribal children. This paper primarily focuses on the challenges of online education among tribal students with a clear emphasis on the English language acquisition. This study was conducted in four most tribal populated districts of the State, namely, Wayanad, Malappuram, Palakkad, and Idukki. This is a qualitative explorative study that explores the experiences of the tribal students' English language learning challenges from the teachers' perspective in these districts. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 19453-19457.
https://doi.org/10.1149/10701.19453ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134040868&doi=10.1149%2f10701.19453ecst&partnerID=40&md5=28673fb73c3fd3875212f955bfa58673
Alexander M.M., Department of English Studies, CHRIST (Deemed to be University), India; John C., Department of Sociology and Social Work, CHRIST (Deemed to be University), India; Babu K.S., Department of English Studies, CHRIST (Deemed to be University), India
Impact of Urban Environmental Quality, Residential Satisfaction, and Personality on Quality of Life among Residents of Delhi/NCR
Environmental quality and Sustainability seek to preserve, enhance and protect our environmental resources that directly aim at providing an amicable quality of life and sustainable development for the upcoming generations. Considering the hazardous environmental urban quality in Delhi NCR, air pollution is the topmost factor deteriorating health of the population in general. The urban air database by WHO reports Delhi exceeding the maximum PM10 limit by almost 10-times at 292 ?g/m3. Noticing that an individual's surroundings have an enormous value in human lives, the study aimed at understanding the impact of urban environmental quality, residential satisfaction, and personality on the quality of life among residents of Delhi NCR. In addition, we also track the environmental worldviews to attitudes on pro-environmental behavior in understanding sustainability. The results from the SEM model indicated that one index rise in RESS lead to a fall in quality of life by 0.029-point value whereas one index rise in personality could enhance the quality of life by 0.15-point value. Pro-Environmental Behaviors and Urban Environmental factors did not showcase any significant impact on the quality of life. The Electrochemical Society.
Chukkali S.; Rastogi K.; Mishra A.; Nayar S.; Gupta T.
ECS Transactions, Vol-107, No. 1, pp. 19875-19887.
https://doi.org/10.1149/10701.19875ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133690492&doi=10.1149%2f10701.19875ecst&partnerID=40&md5=975b4beed34ba05f9d3c67beea2387d3
Chukkali S., Department of Psychology, CHRIST (Deemed to Be University), Delhi NCR, 201003, India; Rastogi K., Department of Psychology, CHRIST (Deemed to Be University), Delhi NCR, 201003, India; Mishra A., Department of Economics, CHRIST (Deemed to Be University), Delhi NCR, 201003, India; Nayar S., Department of Psychology, CHRIST (Deemed to Be University), Delhi NCR, 201003, India, Department of Economics, CHRIST (Deemed to Be University), Delhi NCR, 201003, India; Gupta T., Department of Psychology, CHRIST (Deemed to Be University), Delhi NCR, 201003, India, Department of Economics, CHRIST (Deemed to Be University), Delhi NCR, 201003, India
Sentimental Analysis on Online Education Using Machine Learning Models
Emotion classification; Machine learning; Natural language processing (NLP); Online education; Sentimental analysis
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.
Lecture Notes in Networks and Systems, Vol-462, pp. 413-422.
Mathew S.T., Department of Data Science, Christ University, Bengaluru, India; Jacob L., Department of Data Science, Christ University, Bengaluru, India
Fortitude, and Sense of Coherence in achieving Financial Resilience and Financial Health of Micro and Small Entrepreneurs
The COVID 19 pandemic has brought economic shock s all over the world. India is not an exception to this. The pandemic has made the lives of poor, and downtrodden people, micro, and small entrepreneurs miserable. Micro and small enterprises struggle to bounce back financially and to achieve financial health. Micro and small entrepreneurs face many problems such as no adequate income and savings, debt repayment, rising costs, lack of funds to run the business, financial and mental stress, uncertain future, and so on. Despite these problems, the micro and small enterprises move on steadily to achieve the goal of financial health. What makes them move on steadily? How do they manage their resources to achieve financial resilience? To seek answers to these questions, this study would like to examine the role of fortitude and sense of coherence in achieving financial resilience and financial health of micro and small entrepreneurs. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 405-420.
https://doi.org/10.1149/10701.0405ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130555342&doi=10.1149%2f10701.0405ecst&partnerID=40&md5=97b8d7d5758a54bf311cc7a625ab0226
Ravikumar T., School of Business and Management, CHRIST (Deemed University), Karnataka, Bangalore, 560029, India; Mathew J., School of Business and Management, CHRIST (Deemed University), Karnataka, Bangalore, 560029, India; Aranha R.H., School of Business and Management, CHRIST (Deemed University), Karnataka, Bangalore, 560029, India
Effect of Doping in Aluminium Nitride (AlN) Nanomaterials: A Review
Piezoelectric materials can generate electrical charges when subjected to mechanical pressure through the piezoelectric effect. In addition to generating electricity from environmental vibrations, they are also used as nano energy generators for micro electro mechanical systems (MEMS). Aluminum Nitride (AlN) with a doping element exhibits unique physical and chemical properties. It is used to manufacture many electromechanical devices. They are ideal candidates for many applications, including MEMS resonators and microwave filters, due to their large piezoelectric coefficient and low resistance. A number of material properties led to its selection, including high thermal conductivity, good mechanical strength, high resistance, corrosion resistance, and the largest piezoelectric coefficient. A piezoelectric coefficient d33 characterizes the piezoelectric response of AlN thin films. By doping this material, a wide range of applications have been explored. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 15229-15237.
https://doi.org/10.1149/10701.15229ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130576313&doi=10.1149%2f10701.15229ecst&partnerID=40&md5=f78139de8aff05cfdeeb3ece442ab5f4
SnehaThomas E., Department of Electronics and Communication Engineering, CHRIST (Deemed to be University), Bengaluru, India, Department of Applied Electronics and Instrumentation Engineering, Mount Zion College of Engineering, Kadammanitta, Kerala, India; Ranjith R., Department of Electronics and Communication Engineering, CHRIST (Deemed to be University), Bengaluru, India
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
ECS Transactions, Vol-107, No. 1, pp. 3995-4001.
https://doi.org/10.1149/10701.3995ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130525098&doi=10.1149%2f10701.3995ecst&partnerID=40&md5=f85432638fe5f928c8ce501afafa9ff3
Jyothsna R., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Rohini V., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Paulose J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India
University-Community Collaboration for A Sustainable School-Based Program for The Holistic Education and Wellness of Adolescents
Adolescents have been particularly affected by the COVID-19 pandemic and the closure of schools that are already struggling to carry out their mission of quality education and holistic well-being of students. Research suggests that community-collaborative schools are improving students' academic engagement and reducing learning barriers. When communities and universities are involved in holistic education, it benefits all the stakeholders by enhancing mutual learning and strengthening both. Community members' involvement for student development encourages students and their families to be more involved in community-service initiatives. The paper reports DREAMS, a multi-stakeholder partnership (schools, universities and communities) after-school mentoring model's sustainability. The study identifies and delineates how the model has incorporated the Sustainable Development Goals (SDGs) calling for Good Health and Well-being (SDG-3), Quality Education (SDG-4), Sustainable Cities and Communities (SDG-11) through Partnerships to Achieve its Goals (SDG-17) and proposes it as a sustainable afterschool plan for the post COVID scenario. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 14855-14866.
https://doi.org/10.1149/10701.14855ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130527831&doi=10.1149%2f10701.14855ecst&partnerID=40&md5=4b32fa9014d239974d5c636a038977cc
Thomas L., Department of Psychology, CHRIST (Deemed to be University), Bangalore, India
An Intelligent Recommendation System Using Market Segmentation
Association rule mining; Content-based recommender system; E-Commerce; Hybrid recommender system; Market basket analysis; Market segmentation
Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-303 SIST, pp. 468-476.
Mechery S., Department of Data Science, Christ University, Lavasa, Pune, India; Preethi N., Department of Data Science, Christ University, Lavasa, Pune, India
Do Millennial Exhibit Environmentally Responsive Consumption BehaviorsA Study on Determinants of Green Purchase Decision?
Consumers environmental beliefs; Environmental concerns; Green products; Purchase behavior
The purchase behavior of green products is largely affected by the intention-action gap and skepticism present among consumers. The purpose of this study was to analyze the various factors that affect the purchase behavior of green products among millennials. The practical benefit of this research is that it will assist in the convergence of green marketing and environmental consumer behavior theories. The theory used in the study is the theory of planned behavior. It helps to understand the specific behaviors of consumers as a possibility of a particular behavioral intention. For this purpose, we identified five constructs, namely, Environmental Concerns and Belief (ECB), Eco-Labelling (EL), Green Packaging and Branding (GPB), Green Product, Premium, and Pricing (GPPP), and Consumers Beliefs Towards the Environment (CBTE). These constructs have helped in identifying and analyzing the various factors that affect the purchase behavior of green products among millennials. We analyzed the purchase behavior of green products using a questionnaire approach. For this descriptive study, there were 251 millennials as our respondents who were chosen using the convenience sampling technique. The data was collected through a structured questionnaire via Google form and was analyzed using regression analysis, correlation. It was found that the key factors of green marketing such as Environmental Concerns and Beliefs (ECB), Green Packaging and Branding (GPB), and Green Product, Premium and Pricing (GPPP) have a positive influence on Consumers Beliefs Towards the Environment (CBTE). It implies that by increasing the spending on green packaging and branding there will be a positive effect on consumers environmental beliefs. On the other hand, Eco-Labelling (EL) has a negative influence on Consumers Beliefs Towards the Environment (CBTE) and this is caused by skepticism present among millennials. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Priyadarsini M.K.; PraveenKumar T.; Lakshmi B.A.; Jyotsna S.A.; Swetha A.
Environmental Science and Engineering, pp. 771-784.
Priyadarsini M.K., PSG Institute of Management, Coimbatore, India; PraveenKumar T., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Lakshmi B.A., PSG Institute of Management, Coimbatore, India; Jyotsna S.A., PSG Institute of Management, Coimbatore, India; Swetha A., PSG Institute of Management, Coimbatore, India
Nature's Lament: A Comparative Psychoanalytical Reading of Childhood Trauma in Select War Narratives
Sustainable Development has become an inevitable need of the hour. This paper problematizes the trauma of children as represented in the narratives, Beasts of No Nation by Uzodinma Iweala and A Long Way Gone by Ishmael Beah. The incomprehensibility of trauma, it's varied representation in fiction, dissociation of child psyche, and its detrimental effect on children is substantiated using psychoanalytic theory of trauma proposed by Cathy Caruth and contemporary trauma theorists. The paper argues the atrocities children are forced to be involved into, causes profound trauma in themselves leading to, encumbering of sustainable developmental goals. A comparative study of interpretive textual analysis is employed to study the havoc the society endears as a result of war, that wrecks the child, hindering the overall sustainable development. As it voices out the voiceless trauma of children the paper also aims in divulging the decisive influence of the select literary narratives in sensitizing the society in achieving societal as well as environmental sustainability. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 16867-16878.
https://doi.org/10.1149/10701.16867ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130583248&doi=10.1149%2f10701.16867ecst&partnerID=40&md5=72e04e94c3d011a855affd4b650ac224
Jansy E., Department of English, Christ (Deemed to be) University, Bangalore, 560029, India; Mathews S.P., Department of English, Christ (Deemed to be) University, Bangalore, 560029, India