A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants
Convolutional neural networks (CNN); DenseNet; image processing; Inception; MobileNet; ResNet; VGG; Xception
Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed. 2022 World Scientific Publishing Company.
Chandra N.V.M.; Reddy K.A.; Sushanth G.; Sujatha S.
International Journal of Wavelets, Multiresolution and Information Processing, Vol-20, No. 1
Chandra N.V.M., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, 560074, India; Reddy K.A., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, 560074, India; Sushanth G., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, 560074, India; Sujatha S., Department of Electronics and Communication Engineering, CHRIST University, Bangalore, 560074, India
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry.
Analytical Methods, Vol-16, No. 30, pp. 5263-5271.
https://doi.org/10.1039/d4ay00675e https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200423567&doi=10.1039%2fd4ay00675e&partnerID=40&md5=7a268ed8cd2397bd5624f68ba86208d8
Vishnu S., Department of Chemistry, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Nag A., Department of Life Science, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Das A.K., Department of Chemistry, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India
A Video Surveillance-based Enhanced Collision Prevention and Safety System
collision warning system; enhanced vision-based system; non max suppression; pedestrian detection; vehicle detection; video surveillance; YOLOv3
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023.
14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023, Vol-2023-June, pp. 2346-2354.
Loungani B., Department of Data Science, Christ University, Pune, India; Agrawal J., Department of Data Science, Christ University, Pune, India; Jacob L., Department of Data Science, Christ University, Pune, India
A Voting Enabled Predictive Approach for Hate Speech Detection
CountVectorizer; Hate speech detection; logistic regression; machine learning; Naive Bayes classifier; Random Forest classifier
In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches. 2023 IEEE.
Panda P.; Mishra S.; Sharma V.; Alkhayyat A.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 450-454.
Institute of Electrical and Electronics Engineers Inc.
Panda P., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST (Deemed to Be University), Computer Science Department, Delhi NCR, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
A Way Towards Next-Gen Networking System for the Development of 6G Communication System
6G; AI; challenges; communication services; core services; design plan; networking; performance indices; solutions; supporting technologies
In this talk, the advancements announced by sixth-generation mobile communication (6G) as compared to the earlier fifth-generation (5G) system are carefully examined. The analysis, based in existing academic works, underscores the goal of improving diverse communication aims across various services. This study finds five crucial 6G core services designed to meet distinct goal requirements. To explain these services thoroughly, the framework presents two central features and delineates eight significant performance indices (KPIs). Furthermore, a thorough study of supporting technologies is performed to meet the stated KPIs. A unified 6G design is suggested, imagined as a combination of these supporting technologies. This design plan is then explained by the lens of five prototype application situations. Subsequently, possible challenges contained in the developing track of the 6G network technology are carefully discussed, followed by suggested solutions. The debate ends in an exhaustive examination of possibilities within the 6G world, seeking to provide a strategy plan for future research efforts. 2024 IEEE.
Mohan C.; Singh D.P.; Singh K.R.; Divya N.; Ramkumar R.; Abirami S.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 276-281.
Mohan C., Christ University, School of Business and Management (SB and M), Bangalore, India; Singh D.P., Ies Institute of Technology and Management, Ies University, Electronics & Communication Engineering Department, M.P, Bhopal, India; Singh K.R., Karpagam Academy of Higher Education, Department of Computer Science, TN, India; Divya N., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Civil Engineering (CE), TN, India; Ramkumar R., Bannari Amman Institute of Technology, Department of Ece, Tamil Nadu, Sathyamangalam, India; Abirami S., Hindustan Institute of Technology and Science, Department of Information Technology, Tamil Nadu, Kelambakkam, India
A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model
Anomaly; False information detection; Forensic-based investigation; Misinformation campaign; Online social network (ONS) optimization; WFBIO algorithm
Todays technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration. The Author(s) 2025.
Sethurajan, Monikka Reshmi; Natarajan, K.
Discover Computing;Volume;28;Issue;1;Article No.;279;
Springer Science and Business Media B.V.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1007/s10791-025-09810-z https://www.scopus.com/pages/publications/105022748197?origin=resultslist
Sethurajan M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India; Natarajan K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India
A weighted-Weibull distribution: Properties and applications
brittle strength data; dirichlet eta function; failure data; maximum likelihood estimation; Weibull distribution; wind speed data
The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life areillustrated. 2023 John Wiley & Sons Ltd.
Quality and Reliability Engineering International, Vol-40, No. 2, pp. 863-875.
https://doi.org/10.1002/qre.3439 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170526679&doi=10.1002%2fqre.3439&partnerID=40&md5=fc50942b468ab2318fdb9b385469ebbb
Xavier T., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Nadarajah S., Department of Mathematics, University of Manchester, Manchester, United Kingdom
A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching
attraction; close relationships; human mating; ideals; matching hypothesis
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, mens and (especially) womens stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, mens stated preferences underestimatedand womens stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. 2024 American Psychological Association
Eastwick P.W.; Sparks J.; Finkel E.J.; Meza E.M.; Adamkovi? M.; Adu P.; Ai T.; Akintola A.A.; Al-Shawaf L.; Apriliawati D.; Arriaga P.; Aubert-Teillaud B.; Ban G.; Barzykowski K.; Batres C.; Baucom K.J.; Beaulieu E.Z.; Behnke M.; Butcher N.; Charles D.Y.; Chen J.M.; Cheon J.E.; Chittham P.; Chwi?kowska P.; Cong C.W.; Copping L.T.; Corral-Frias N.S.; Adori? V.?.; Dizon M.; Du H.; Ehinmowo M.I.; Escribano D.A.; Espinosa N.M.; Expito F.; Feldman G.; Freitag R.; Armenta M.F.; Gallyamova A.; Gillath O.; Gjoneska B.; Gkinopoulos T.; Grafe F.; Grigoryev D.; Groyecka-Bernard A.; Gunaydin G.; Ilustrisimo R.; Impett E.; Ka?m P.; Kim Y.-H.; Kocur M.; Kowal M.; Krishna M.; Labor P.D.; Lu J.G.; Lucas M.Y.; Ma?ecki W.P.; Malinakova K.; Meier S.; Meier Z.; Misiak M.; Muise A.; Novak L.; Jiaqing O.; do?ru A.A.; Park H.G.; Paruzel M.; Pavlovi? Z.; Pki M.; Ribeiro G.; Roberts S.C.; Rr J.P.; Ropovik I.; Ross R.M.; Sakman E.; Salvador C.E.; Selcuk E.; Skakoon-Sparling S.; Sorokowska A.; Sorokowski P.; Spasovski O.; Stanton S.C.E.; Stewart S.L.K.; Swami V.; Szaszi B.; Takashima K.; Tavel P.; Tejada J.; Tu E.; Tuominen J.; Vaidis D.; Vally Z.; Vaughn L.A.; Villanueva-Moya L.; Wisnuwardhani D.; Yamada Y.; Yonemitsu F.; kovR.; ivnK.; Coles N.A.
Journal of Personality and Social Psychology
https://doi.org/10.1037/pspp0000524 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208261406&doi=10.1037%2fpspp0000524&partnerID=40&md5=acc575d872be7255db465099bd6640c6
Eastwick P.W., Department of Psychology, University of California, Davis, United States; Sparks J., Behavioral Decision Making Group, University of California, Los Angeles Anderson School of Management, United States; Finkel E.J., Department of Psychology, Northwestern University, United States, Kellogg School of Management, Northwestern University, United States, Institute for Policy Research, Northwestern University, United States; Meza E.M., Department of Psychology, University of California, Davis, United States; Adamkovi? M., Centre of Social and Psychological Sciences, Slovak Academy of Sciences, Slovakia, Faculty of Education, Charles University, Czech Republic, Faculty of Humanities and Social Sciences, University of Jyvkyl Finland; Adu P., Wellington Faculty of Health, School of Health, Victoria University of Wellington, New Zealand; Ai T., Department of Social Psychology, University of Kansas, United States; Akintola A.A., Department of Psychology, Redeemers University, Nigeria; Al-Shawaf L., Department of Psychology, University of Colorado, Colorado Springs, Colorado Springs, United States, Lyda Hill Institute for Human Resilience, University of Colorado, Colorado Springs, United States, Institute for Advanced Study in Toulouse (IAST), France; Apriliawati D., Department of Psychology, Universitas Islam Negeri Sunan Kalijaga, Indonesia; Arriaga P., Department of Social and Organizational Psychology, Iscte-University Institute of Lisbon, Portugal; Aubert-Teillaud B., Institut de Psychologie, UniversitParis Cit France; Ban G., Department of Educational Psychology and Psychology of Health, Pavol Jozef Safarik University, Slovakia; Barzykowski K., Faculty of Philosophy, Institute of Psychology, Jagiellonian University, Poland; Batres C., Department of Psychology, Franklin and Marshall College, United States; Baucom K.J., Department of Psychology, University of Utah, United States; Beaulieu E.Z., Department of Psychology, University of Utah, United States; Behnke M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland, Cognitive Neuroscience Center, Adam Mickiewicz University, Poland; Butcher N., Department of Psychology, Teesside University, United Kingdom; Charles D.Y., Department of Psychology, Christ University, India; Chen J.M., Department of Psychology, Wellesley College, United States; Cheon J.E., Department of Psychology, Yonsei University, South Korea; Chittham P., Faculty of Psychology, Chulalongkorn University, Thailand; Chwi?kowska P., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Cong C.W., Department of Social Science, Faculty of Social Science and Humanities, Tunku Abdul Rahman University of Management and Technology, Malaysia; Copping L.T., Department of Psychology, Teesside University, United Kingdom; Corral-Frias N.S., Department of Psychology, Universidad de Sonora, Mexico; Adori? V.?., Department of Psychology, University of Zadar, Croatia; Dizon M., Department of Psychology, University of Edinburgh, United Kingdom; Du H., Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, China; Ehinmowo M.I., Department of Psychology, University of Ibadan, Nigeria; Escribano D.A., Department of Social Psychology, University of Kansas, United States; Espinosa N.M., Department of Psychology and Neuroscience, Duke University, United States; Expito F., Departament of Social Psychology, University of Granada, Spain; Feldman G., Department of Psychology, University of Hong Kong, Hong Kong; Freitag R., Departamento de Letras Vernulas, Universidade Federal de Sergipe, Brazil; Armenta M.F., Department of Law, Universidad de Sonora, Mexico; Gallyamova A., Center for Sociocultural Research, HSE University, Russian Federation; Gillath O., Department of Social Psychology, University of Kansas, United States; Gjoneska B., Devision of Neuroinformatics in Function of Sustainable Development, Macedonian Academy of Sciences and Arts, North Macedonia; Gkinopoulos T., Institute of Psychology, Jagiellonian University in Krakow, Poland; Grafe F., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Grigoryev D., Center for Sociocultural Research, HSE University, Russian Federation; Groyecka-Bernard A., Institute of Psychology, University of Wroclaw, Poland; Gunaydin G., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Ilustrisimo R., Department of Psychology, University of San Carlos, Philippines; Impett E., Department of Psychology, University of Toronto, Canada; Ka?m P., Department of Psychology, Faculty of Arts, Pavol Jozef afik University in Koice, Slovakia; Kim Y.-H., Department of Psychology, Yonsei University, South Korea; Kocur M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Kowal M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Krishna M., Karnataka, Bangalore, India; Labor P.D., Department of Psychology, University of the Philippines, Philippines; Lu J.G., Sloan School of Management, Massachusetts Institute of Technology, United States; Lucas M.Y., Department of Anthropology and Psychology, Universidad de Sonora, Mexico; Ma?ecki W.P., IDN Being Human, Institute of Polish Studies, University of Wroclaw, Poland; Malinakova K., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Meier S., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Meier Z., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Misiak M., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland, School of Anthropology and Museum Ethnography, University of Oxford, United Kingdom; Muise A., Department of Psychology, York University, Canada; Novak L., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Jiaqing O., Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore; do?ru A.A., Department of Psychology, Marmara University, Turkey, Department of Psychology, kar University, Turkey; Park H.G., Department of Psychology, University of Toronto, Canada; Paruzel M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Pavlovi? Z., Faculty of Philosophy, Department of Psychology, University of Belgrade, Serbia; Pki M., Institute of Psychology, Ev Lord University, Hungary; Ribeiro G., School of Psychology, The University of Queensland, Australia, School of Law and Justice, University of Southern Queensland, Australia; Roberts S.C., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland, Division of Psychology, University of Stirling, United Kingdom; Rr J.P., Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany; Ropovik I., Institute for Research and Development of Education, Faculty of Education, Charles University, Czech Republic, Department of Preschool and Elementary Education and Psychology, Faculty of Education, University of Presov, Slovakia; Ross R.M., Department of Philosophy, Macquarie University, Australia; Sakman E., Department of Psychology, Bilkent University, Turkey; Salvador C.E., Department of Psychology and Neuroscience, Duke University, United States; Selcuk E., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Skakoon-Sparling S., Department of Psychology, Toronto Metropolitan University, Canada; Sorokowska A., Institute of Psychology, University of Wroclaw, Poland; Sorokowski P., IDN Being Human, Institute of Psychology, University of Wroclaw, Poland; Spasovski O., Department of Psychology, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, North Macedonia; Stanton S.C.E., Department of Psychology, University of Edinburgh, United Kingdom; Stewart S.L.K., School of Psychology, University of Chester, United Kingdom; Swami V., School of Psychology, Sport, and Sensory Sciences, Anglia Ruskin University, United Kingdom, Centre for Psychological Medicine, Perdana University, Malaysia; Szaszi B., Institute of Psychology, Ev Lord University, Hungary; Takashima K., Graduate School of Human-Environment Studies, Kyushu University, Japan; Tavel P., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Tejada J., Departamento de Psicologia, Universidade Federal de Sergipe, Brazil; Tu E., Department of Psychology, York University, Canada; Tuominen J., Department of Psychology and Speech-Language Pathology, University of Turku, Finland; Vaidis D., Department of Psychology, Centre for Learning Leadership and Excellence, Centre national de la recherche scientifique, Universitde Toulouse, France; Vally Z., Department of Clinical Psychology, Faculty of Medicine and Health Sciences, United Arab Emirates University, United Arab Emirates; Vaughn L.A., Department of Psychology, Ithaca College, United States; Villanueva-Moya L., Departament of Social Psychology, University of Granada, Spain; Wisnuwardhani D., Faculty of Psychology, Universitas Indonesia, Indonesia; Yamada Y., Faculty of Arts and Science, Kyushu University, Japan; Yonemitsu F., College of Engineering, Shibaura Institute of Technology, Japan; kovR., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; ivnK., Olomouc University Social Health Institute, PalackUniversity Olomouc, Czech Republic; Coles N.A., Center for the Study of Language and Information, Stanford University, United States
A worldwide test of the predictive validity of ideal partner preference matching.
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men's and (especially) women's stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men's stated preferences underestimatedand women's stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved) 2024 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Eastwick, Paul W.; Sparks, Jehan; Finkel, Eli J.; Meza, Eva M.; Adamkovi?, Mat Adu, Peter; Ai, Ting; Akintola, Aderonke A.; Al-Shawaf, Laith; Apriliawati, Denisa; Arriaga, Patria; Aubert-Teillaud, Benjamin; Ban, Gabriel; Barzykowski, Krystian; Batres, Carlota; Baucom, Katherine J.; Beaulieu, Elizabeth Z.; Behnke, Maciej; Butcher, Natalie; Charles, Deborah Y.; Chen, Jane Minyan; Cheon, Jeong Eun; Chittham, Phakkanun; Chwi?kowska, Patrycja; Cong, Chin Wen; Copping, Lee T.; Corral-Frias, Nadia S.; ?ubela Adori?, Vera; Dizon, Mikaela; Du, Hongfei; Ehinmowo, Michael I.; Escribano, Daniela A.; Espinosa, Natalia M.; Expito, Francisca; Feldman, Gilad; Freitag, Raquel; Frias Armenta, Martha; Gallyamova, Albina; Gillath, Omri; Gjoneska, Biljana; Gkinopoulos, Theofilos; Grafe, Franca; Grigoryev, Dmitry; Groyecka-Bernard, Agata; Gunaydin, Gul; Ilustrisimo, Ruby; Impett, Emily; Ka?m, Pavol; Kim, Young-Hoon; Kocur, Miros?aw; Kowal, Marta; Krishna, Maatangi; Labor, Paul Danielle; Lu, Jackson G.; Lucas, Marc Y.; Ma?ecki, Wojciech P.; Malinakova, Klara; Meier, Sofia; Meier, Zden?k; Misiak, Michal; Muise, Amy; Novak, Lukas; O, Jiaqing; do?ru, Asil A.; Park, Haeyoung Gideon; Paruzel, Mariola; Pavlovi?, Zoran; Pki, Marcell; Ribeiro, Gianni; Roberts, S. Craig; Rr, Jan P.; Ropovik, Ivan; Ross, Robert M.; Sakman, Ezgi; Salvador, Cristina E.; Selcuk, Emre; Skakoon-Sparling, Shayna; Sorokowska, Agnieszka; Sorokowski, Piotr; Spasovski, Ognen; Stanton, Sarah C. E.; Stewart, Suzanne L. K.; Swami, Viren; Szaszi, Barnabas; Takashima, Kaito; Tavel, Peter; Tejada, Julian; Tu, Eric; Tuominen, Jarno; Vaidis, David; Vally, Zahir; Vaughn, Leigh Ann; Villanueva-Moya, Laura; Wisnuwardhani, Dian; Yamada, Yuki; Yonemitsu, Fumiya; kov Radka; ivn Krista; Coles, Nicholas A.
Journal of Personality and Social Psychology;Volume;128;Issue;1;pp.123-146
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1037/pspp0000524 https://www.scopus.com/pages/publications/85208261406?origin=resultslist
Eastwick P.W., Department of Psychology, University of California, Davis, Davis, CA, United States; Sparks J., Behavioral Decision Making Group, University of California, Los Angeles, Anderson School of Management, Los Angeles, CA, United States; Finkel E.J., Department of Psychology, Northwestern University, Evanston, IL, United States; Meza E.M., Department of Psychology, University of California, Davis, Davis, CA, United States; Adamkovi? M., Centre of Social and Psychological Sciences, Slovak Academy of Sciences, Slovakia; Adu P., Wellington Faculty of Health, School of Health, Victoria University of Wellington, Wellington, New Zealand; Ai T., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Akintola A.A., Department of Psychology, Redeemer's University, Nigeria; Al-Shawaf L., Department of Psychology, University of Colorado, Colorado Springs, CO, United States; Apriliawati D., Department of Psychology, Universitas Islam Negeri Sunan Kalijaga, Indonesia; Arriaga P., Department of Social and Organizational Psychology, Iscte-University Institute of Lisbon, Lisbon, Portugal; Aubert-Teillaud B., Institut de Psychologie, Universite Paris Cite, Paris, France; Ban G., Department of Educational Psychology and Psychology of Health, Pavol Jozef Safarik University, Slovakia; Barzykowski K., Faculty of Philosophy, Institute of Psychology, Jagiellonian University, Poland; Batres C., Department of Psychology, Franklin and Marshall College, Lancaster, PA, United States; Baucom K.J., Department of Psychology, University of Utah, Salt Lake City, UT, United States; Beaulieu E.Z., Department of Psychology, University of Utah, Salt Lake City, UT, United States; Behnke M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Butcher N., Department of Psychology, Teesside University, United Kingdom; Charles D.Y., Department of Psychology, Christ University, India; Chen J.M., Department of Psychology, Wellesley College, Wellesley, MA, United States; Cheon J.E., Department of Psychology, Yonsei University, South Korea; Chittham P., Faculty of Psychology, Chulalongkorn University, Thailand; Chwi?kowska P., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Cong C.W., Department of Social Science, Faculty of Social Science and Humanities, Tunku Abdul Rahman University of Management and Technology, Malaysia; Copping L.T., Department of Psychology, Teesside University, United Kingdom; Corral-Frias N.S., Department of Psychology, Universidad de Sonora, Mexico; ?ubela Adori? V., Department of Psychology, University of Zadar, Zadar, Croatia; Dizon M., Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Du H., Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Beijing, China; Ehinmowo M.I., Department of Psychology, University of Ibadan, Ibadan, Nigeria; Escribano D.A., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Espinosa N.M., Department of Psychology and Neuroscience, Duke University, Durham, NC, United States; Expito F., Departament of Social Psychology, University of Granada, Granada, Spain; Feldman G., Department of Psychology, University of Hong Kong, Hong Kong; Freitag R., Departamento de Letras Vernaculas, Universidade Federal de Sergipe, Brazil; Frias Armenta M., Department of Law, Universidad de Sonora, Mexico; Gallyamova A., Center for Sociocultural Research, HSE University, Russian Federation; Gillath O., Department of Social Psychology, University of Kansas, Lawrence, KS, United States; Gjoneska B., Devision of Neuroinformatics in Function of Sustainable Development, Macedonian Academy of Sciences and Arts, North Macedonia; Gkinopoulos T., Institute of Psychology, Jagiellonian University in Krakow, Krakow, Poland; Grafe F., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Grigoryev D., Center for Sociocultural Research, HSE University, Russian Federation; Groyecka-Bernard A., Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Gunaydin G., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Ilustrisimo R., Department of Psychology, University of San Carlos, Philippines; Impett E., Department of Psychology, University of Toronto, Toronto, ON, Canada; Ka?m P., Department of Psychology, Faculty of Arts, Pavol Jozef Safarik University in Kosice, Kosice, Slovakia; Kim Y.-H., Department of Psychology, Yonsei University, South Korea; Kocur M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Kowal M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Krishna M.; Labor P.D., Department of Psychology, University of the Philippines, Philippines; Lu J.G., Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States; Lucas M.Y., Department of Anthropology and Psychology, Universidad de Sonora, Mexico; Ma?ecki W.P., IDN Being Human, Institute of Polish Studies, University of Wroclaw, Wroclaw, Poland; Malinakova K., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Meier S., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Meier Z., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Misiak M., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Muise A., Department of Psychology, York University, North York, ON, Canada; Novak L., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; O J., Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore; do?ru A.A., Department of Psychology, Marmara University, Turkey; Park H.G., Department of Psychology, University of Toronto, Toronto, ON, Canada; Paruzel M., Department of Psychology and Cognitive Science, Adam Mickiewicz University, Poland; Pavlovi? Z., Faculty of Philosophy, Department of Psychology, University of Belgrade, Belgrade, Serbia; Pki M., Institute of Psychology, Eotvos Lorand University, Hungary; Ribeiro G., School of Psychology, University of Queensland, Brisbane, QLD, Australia; Roberts S.C., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Rr J.P., Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany; Ropovik I., Institute for Research and Development of Education, Faculty of Education, Charles University, Czech Republic; Ross R.M., Department of Philosophy, Macquarie University, North Ryde, NSW, Australia; Sakman E., Department of Psychology, Bilkent University, Turkey; Salvador C.E., Department of Psychology and Neuroscience, Duke University, Durham, NC, United States; Selcuk E., Psychology Program, Faculty of Arts and Social Sciences, Sabanci University, Turkey; Skakoon-Sparling S., Department of Psychology, Toronto Metropolitan University, Toronto, ON, Canada; Sorokowska A., Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Sorokowski P., IDN Being Human, Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Spasovski O., Department of Psychology, Faculty of Philosophy, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia; Stanton S.C.E., Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Stewart S.L.K., School of Psychology, University of Chester, Chester, United Kingdom; Swami V., School of Psychology, Sport, and Sensory Sciences, Anglia Ruskin University, United Kingdom; Szaszi B., Institute of Psychology, Eotvos Lorand University, Hungary; Takashima K., Graduate School of Human-Environment Studies, Kyushu University, Japan; Tavel P., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Tejada J., Departamento de Psicologia, Universidade Federal de Sergipe, Brazil; Tu E., Department of Psychology, York University, North York, ON, Canada; Tuominen J., Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland; Vaidis D., Department of Psychology, Centre for Learning Leadership and Excellence, centre national de la recherche scientifique, Universite de Toulouse, Toulouse, France; Vally Z., Department of Clinical Psychology, Faculty of Medicine and Health Sciences, United Arab Emirates University, United Arab Emirates; Vaughn L.A., Department of Psychology, Ithaca College, Ithaca, NY, United States; Villanueva-Moya L., Departament of Social Psychology, University of Granada, Granada, Spain; Wisnuwardhani D., Faculty of Psychology, Universitas Indonesia, Indonesia; Yamada Y., Faculty of Arts and Science, Kyushu University, Japan; Yonemitsu F., College of Engineering, Shibaura Institute of Technology, Japan; kovR., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; ivnK., Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic; Coles N.A., Center for the Study of Language and Information, Stanford University, Stanford, CA, United States
AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques
Accident Detection; Computer Vision; Fast SPPF; Fine Tune Augment YOLOv10; PSA
Computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. 2025 IEEE.
Karthikeyan, M.; Kshirsagar, Pravin Ramdas; Tak, Tan Kuan; Lalit, Keshav; Upreti, Kamal; Jain, Rituraj
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.1351-1356
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/IC363308.2025.10956514 https://www.scopus.com/pages/publications/105003903914?origin=resultslist
Karthikeyan M., School of Computing, Srm Institute of Science and Technology, Dept. of Computing Technologies, Chennai, India; Kshirsagar P.R., J.D College of Engg & Mgmt., Department of Electronics Engg., MS, Nagpur, India; Tak T.K., Singapore Institute of Technology, Singapore; Lalit K., Manipal Institute of Technology, Dept. of Computer Science & Engineering, Karnataka, Manipal, India; Upreti K., Christ University, Department of Computer Science, Delhi NCR, Ghaziabad, India; Jain R., Marwadi University, Department of Info. Tech., Gujarat, Rajkot, India
Abelian-Type Results for the Mexican Hat Wavelet Transform of Compactly Supported Distributions
compact support; distributions; wavelet transform and Mexican hat wavelet transform
In this paper, we introduce a distribution space that extends the framework of the Abelian theorems to the Mexican hat wavelet transform (MHWT) of distributions. We establish two Abelian theorems for the MHWT applied to compactly supported distributions and for locally integrable functions, providing new insights into their asymptotic behavior. 2025 John Wiley & Sons Ltd.
Mathematical Methods in the Applied Sciences;Volume;49;Issue;5;pp.4340-4349
https://doi.org/10.1002/mma.70349 https://www.scopus.com/pages/publications/105022698837?origin=resultslist
Rawat A., School of Sciences, CHRIST (Deemed to be University), Bengaluru, India; Singh A., School of Mathematics, Faculty of Sciences, Shri Mata Vaishno Devi University, J&K, Katra, India
Abjection and Intersecting Trans Women Identities: Examining Doing Gender through Malayalam Movies Ardhanaari and Njan Marykutty; [Abjeo e Identidades de Mulheres Trans Interseccionadas: Examinando Fazendo Gero atrav dos Filmes em Malayalam Ardhanaari e Njan Marykutty]; [Examinando hacer gero a trav de las pelulas en malayalam Ardhanaari y Njan Marykutty]
Abjection; cinema; gender; identity; trans women
The non-confirmation to vexed societal gender norms places trans identities in an abjected state. Media, mainly cinema, plays an indispensable role in shaping, shunning, and promulgating such ideologies. To understand this discourse, the Malayalam films Ardhanaari (2015) and Njan Marykutty (2018) are taken to examine the question of abjection, a concept by Kristeva, and doing gender, by West and Zimmerman. The study argues that the abjection trans identities face forces them to perform their gender in accordance with cisnormative femininity. The study further argues that trans identities should embrace abjection and employ it as a political tool to disrupt the established hegemonic traditional gender structure and its definitions. 2023 Universidad de Guadalajara. All rights reserved.
Comunicacion y Sociedad (Mexico), No. 20
https://doi.org/10.32870/cys.v2023.8484 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165116175&doi=10.32870%2fcys.v2023.8484&partnerID=40&md5=b509a0112082ad1bba77b6908fe92151
Varghese A.M., Christ University, India; Tanupriya, Christ University, India
ABL as a linguistic pedagogy-An experimental study of acquisition of English language among primary school children
The history of English language teaching has been regarded as a pursuit for more effective ways of teaching and learning. The teaching profession has held constant discussions and debates over issues like framing effective learning strategies,techniques for teaching the four skills to the students, choice of curriculum frameworks, materials available for effective English language teaching, role of grammar and vocabulary in English classrooms, teaching English language using technology, improvement of fluency and accuracy during the process of language learning and so on. English language experts are constantly engaged in clarifying these areas including the search for an appropriate approach of teaching English language effectively in classrooms.
ABL as a linguistic pedagogy-an experimental study of acquisition of english language among primary school children
Ableisms Impact on Body and Identity in Indra Sinhas Animals People
Ableism and disability; ableness and normalcy; body image; dehumanisation; identity crisis; othering; self-image; social construction
This study explores the link between ableism and identity formation through the discourse analysis of Indra Sinhas Animals People (2007). Set against the backdrop of a post-industrial disaster, the Bhopal gas tragedy in an Indian metropolis, the novel provides a powerful narrative about the marginalised. By analysing the social perceptions and structures that define ability, normalcy, and physical beauty, the study investigates how societal norms and cultural attitudes frame the protagonists experiences of exclusion and identity crisis. The study further investigates how the novel critiques the broader social and cultural dimensions of ableism in post-colonial contexts, revealing the intersection of power, disability, and identity in the social fabric of Asian societies. Social constructionist perspectives provide a framework for comprehending the social construction of disability, stigma, othering of disabled bodies, and cultural norms of beauty, normalcy, ability, and identity. Subjective and objective realities are discussed around the character, Animal. The study findings reveal the profound personal consequence of ableism on the self-image, body image, and self-perception of individuals with disabilities, the dehumanisation, marginalisation, and ultimately, an identity crisis of disabled individuals. 2025 International Islamic University Malaysia. All rights reserved.
Job, Mary T.; Thomas, Christine Ann; Prakasha, G.S.
International Islamic University Malaysia
All Open Access; Hybrid Gold Open Access
https://doi.org/10.31436/asiatic.v19i1.3649 https://www.scopus.com/pages/publications/105008004985?origin=resultslist
Job M.T., Christ University, Central Campus, Bangalore, India; Thomas C.A., Department of English and Cultural Studies, Christ University, Central Campus, Bangalore, India; Prakasha G.S., School of Education, Christ University, Bangalore, India
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
Abuse words; CNN; Gradient Boosting Machine and random forest; SVM; XGB classifier
Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions. 2024 IEEE.
Madhurima S.; Abhijith Ajith K.; Vs S.; Prathap B.R.
Proceedings - 2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024, pp. 312-317.
https://doi.org/10.1109/DICCT61038.2024.10532806 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195168310&doi=10.1109%2fDICCT61038.2024.10532806&partnerID=40&md5=4a21e8cff5fccbe31ba79b9e5f98df78
Madhurima S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Abhijith Ajith K., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Vs S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Prathap B.R., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India
Acacia auriculiformisDerived Bimodal Porous Nanocarbons via Self-Activation for High-Performance Supercapacitors
organic electrolyte; porous carbon; pyrolysis; self-activation; supercapacitors
Carbon nanomaterials derived from Acacia auriculiformis pods as electrodes for the electrochemical double-layer capacitors were explored. Four pyrolysis temperatures were set (400, 600, 800, and 1,000C) to understand the role of temperature in biomass pyrolysis via a possible self-activation mechanism for the synthesis of carbon materials. The carbon materials synthesized at 800C (AAC800) were found to exhibit a well-organized hierarchical porous structure, quantified further from N2 adsorption/desorption isotherms with a maximum specific surface area of 736.6m2/g. Micropores were found to be contributing toward enhancing the specific surface area. AAC800 exhibited a maximum specific capacitance of 176.7F/g at 0.5A/g in 6.0M KOH electrolyte in a three-electrode setup. A symmetric supercapacitor was fabricated using AAC800 as an active material in an organic electrolyte composed of 1.0M tetraethylammonium tetrafluoroborate (TEABF4) as a conducting salt in the acetonitrile (ACN) solvent. The self-discharge of the cell/device was analyzed from fitting two different mathematical models; the cell also exhibited a remarkable coulombic efficiency of 100% over 10,000 charge/discharge cycles, retaining ?93% capacitance at 2.3V. Copyright 2021 Bhat, Jayeoye, Rujiralai, Sirimahachai, Chong and Hegde.
Bhat V.S.; Jayeoye T.J.; Rujiralai T.; Sirimahachai U.; Chong K.F.; Hegde G.
https://doi.org/10.3389/fenrg.2021.744133 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117115763&doi=10.3389%2ffenrg.2021.744133&partnerID=40&md5=3d60ae24b10dd13346124601e9b3a469
Bhat V.S., Centre for Nano-materials and Displays (CND), B.M.S. College of Engineering, Bangalore, India, Department of Materials Science, Mangalore University, Mangalore, India; Jayeoye T.J., Division of Physical Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand; Rujiralai T., Division of Physical Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand; Sirimahachai U., Division of Physical Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand; Chong K.F., Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Gambang, Malaysia; Hegde G., Centre for Nano-materials and Displays (CND), B.M.S. College of Engineering, Bangalore, India, CHRIST (Deemed to be University), Bengaluru, India
Academic Certificate Validation Using Blockchain Technology
Academic certificates; Blockchain; end to end encryption; Fake academic certificates; Hyperledger Fabric; Security; Tampering
Academic certificates are essential for an individual's career and hence they are more prone to being tampered. This paper proposes an idea of sharing certificates and verifying their authenticity using blockchain technology. Blockchain paves the way for secure storage and sharing of information. Its main focus is to maintain trust among users. This proposal focuses on designing and implementing a system that will prove to be a solution for addressing the issue of fake certificates using Hyperledger Fabric. The technology here is tamper-proof and maintains transparency. This system will have a database of academic certificates awarded by the University, which is recorded as a transaction using the Hyperledger Fabric, which further can be referred by other organizations present in the network to verify the authenticity of the certificates using the information provided by the students to the database. This system provides end to end encryption. 2022 IEEE.
Sethia G.; Namratha S.; Srikanth H.; Sreeja S.C.
2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022
Sethia G., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Namratha S., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Srikanth H., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India; Sreeja S.C., CHRIST(Deemed to Be University), Department of Computer Science, Bengaluru, India
Academic leader behaviour influence tactics in relation to organizational commitment and work engagement of faculty in higher educational institutions
The importance of academic leader behaviour and influence tactics needs to be overemphasized, as these are the most essential components of practically every newlineeducational institution. A clear understanding of educational institution requires a thorough analysis of academic leader behaviour and influence tactics as main features. Academic leaders regularly acquire and use power. They do so newlinedeliberately and consciously as well as intuitively and unconsciously. Leadership newlineand power do differ in goal compatibility, direction of influence on one s newlinesubordinates and research emphasis. This study attempts to reduce drastically these newlinedifferences and focus on the positives of influence tactics and leadership processes newlineto be exercised by higher authorities for enhancing institutional effectiveness. newlineThe present investigation focused on understanding the leader s behaviour and influence tactics adopted by individuals when they hold power positions, how it hinders the growth of individuals and institutions goals. Faculty Organisational Commitment, Work Engagement and the intention of stay/leave the institution of both academic leaders and faculty members working for higher educational institutions (Engineering, MBA and MCA colleges) were involved in thorough newlineinvestigation. The dependent variables were work engagement and Organisational newlinecommitment. Five tools were adopted to collect data. Leader Behaviour Description Questionnaire (1962) developed by staff members of Fisher College of Business, Ohio State Leadership Studies, Influence Behaviour Questionnaire (2002) developed by Gary Yukl, Organizational Commitment Questionnaire newline(1991) developed by Meyer and Allen, Utrecht Work Engagement Scale (2003) developed by Schaufeli et al., and Intention to stay/leave tool developed by Dilyis Robinson. The Cronbach Alpha reliability for Leadership Behaviour Description Questionnaire (LBDQ) was 0.907; Influence Behaviour Questionnaire (IBQ) was 0.677 for academic leaders.
Academic leader behaviour, influence tactics in relation to organisational commitment and work engagement of faculty in higher educational institutions
The importance of academic leader behaviour and influence tactics needs to be overemphasized, as these are the most essential components of practically every educational institution. A clear understanding of educational institution requires a thorough analysis of academic leader behaviour and influence tactics as main features. Academic leaders regularly acquire and use power. They do so deliberately and consciously as well as intuitively and unconsciously. Leadership and power do differ in goal compatibility, direction of influence on one’s subordinates and research emphasis. This study attempts to reduce drastically these differences and focus on the positives of influence tactics and leadership processes to be exercised by higher authorities for enhancing institutional effectiveness.