A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein
Molecular docking; Pharmacophore; Phytochemical; Statistics; Virus
The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and ?8.66 kcal mol?1, respectively), when compared to the control drug, Ribavirin (-7.01 and ?6.52 kcal mol?1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol?1) and NiV M (-40.26 kJ mol?1) proteins in comparison with the control drug Ribavirin (-13.12 and ?13.94 kJ mol?1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection. 2024 Elsevier Ltd
Computational Biology and Chemistry, Vol-112
https://doi.org/10.1016/j.compbiolchem.2024.108135 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197028521&doi=10.1016%2fj.compbiolchem.2024.108135&partnerID=40&md5=fc61db524ba4440565b9aae1e95da8a6
Rababi D., Department of Life Sciences, Christ University (Deemed to be University), Karnataka, Bangalore, 560029, India; Nag A., Department of Life Sciences, Christ University (Deemed to be University), Karnataka, Bangalore, 560029, India
A Translator for Indian Sign Boards to English using Tesseract and SEQ2SEQ Model
seq2seq model and Tamil OCR.; tesseract OCR engine
Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in Indic scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with an accuracy of 87%. 2021 IEEE.
2021 International Conference on Simulation, Automation and Smart Manufacturing, SASM 2021
Institute of Electrical and Electronics Engineers Inc.
Sundara Pandiyan S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to Be University), Bangalore, India; Kelvin C., Lg Soft India Pvt. Ltd, Bangalore, India
A Trust Model for Cloud Security and Privacy for Startups in India
The great advantage of cloud computing is elasticity, the ability to add capacity or applications almost at a moments notice. Companies buy exactly the amount of storage, computing power, security and other IT functions that they need from specialists in data-centre computing. On the other side, there are also some disadvantages to using cloud computing that must be considered. In the cloud, the customer may not have the kind of control over their data or the performance of the applications that they need, or the ability to audit or change the processes and policies under which users must work. The security of cloud, and associated privacy concerns, give many organizations pause as they think through their particular cloud computing concerns. Security and privacy concerns include physical security and simple access to facilities and equipment, as well as logical security, industry compliance requirements, audit ability, and more.
A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image
Image Noise Cancellation; Image processing; Image Restoration; Salt and Pepper Noise; Visual quality
In a digital era, Image recuperation plays a vital role in the area of digital image processing. Image instauration offers more visualization on the quality of the image thereby eliminating noise. Elimination of Gaussian and impulse noise is a challenging problem in the area of image restoration. Rigorous research is pursued to restore salt-and-pepper (SAP) noise utilizing spatial filters. Mean and Median are two contributing spatial filters for eliminating impulse noise. This paper applies a two-pass hybrid mean and median framework on a corrupted grayscale image to replace salt and pepper noise. The hybrid framework is effectively restoring the image by abstracting the low, medium, and high-density impulse noise. The efficacy of the recommended strategy is evaluated by quantifying the peak signal to noise ratio and structural similarity index metric. The result obtained when compared with recent recuperation strategies outperforms to remove noise from grayscale images. 2021 IEEE
ACCESS 2021 - Proceedings of 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, pp. 206-210.
https://doi.org/10.1109/ACCESS51619.2021.9563285 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126264458&doi=10.1109%2fACCESS51619.2021.9563285&partnerID=40&md5=70fe12740afbbfdd912b2f5f8253ef8b
Shiju Thomas M.Y., Department of Computer Science and Engineering, School of Engineering and Technology Christ (Deemed to be) University, Karnataka, Bangalore, India; Krishna A.V.N., Department of Computer Science and Engineering, School of Engineering and Technology Christ (Deemed to be) University, Karnataka, Bangalore, India
A two-stepped feature engineering process for topic modeling using batchwise LDA with stochastic variational inference model
Feature engineering; Feature engineering; Hierarchical dirichlet process; Latent dirichlet allocation; Latent semantic analysis; Sentiment analysis; Stochastic variational inference; Topic modeling; Vector space model
Online ratings and customer feedback on hotel booking websites support the decision-making process of the customer as the reviews provide a deeper understanding about all aspects of a hotel. Consequently, review and rating analyses are of great interest to consumers and hotel owners for the hotel related social media services. The key challenge, however, is to make the wide variety of information accessible in a simple, fast and relevant way and the solution is Topic Modelling and Opinion Mining. Common approaches like Latent Semantic Analysis (LSA) and Hierarchical Dirichlet Process (HDP) have order affects. If the input dataset is shuffled then different topics are generated leading to misleading results. To overcome this, a two-stepped feature engineering process is used: first step is to use a TF-IDF with modified trigrams calculation followed by the second step in removing weak features from the corpus thereby reducing the dimensionality of the Vector Space Model (SVM) for efficient Topic Modeling and sentiment analysis of the considered corpus. Sentiment score is calculated using VADER tool and Topic Modeling is done with Batch Wise Latent Dirichlet Allocation (LDA) using Stochastic Variational Inference (SVI) model. The modified trigrams included calculation of probabilities of words not only in the backward direction but also the probability calculation of the next two words of the target word thereby retaining its context information. The proposed method using Batchwise LDA with SVI along with two-stepped feature engineering process considerably improved its performance when compared to LSA and HDP models due to the fact of identifying hidden and relevant topics in terms of their optimized posterior distribution in hotel reviews dataset. The Batchwise LDA with SVI improved its performance by 3% in terms of its coherence values by using two-stepped feature engineering process and by 9% and 4% increase when compared with LSA and HDP models respectively. 2020, Intelligent Network and Systems Society.
International Journal of Intelligent Engineering and Systems, Vol-13, No. 4, pp. 333-345.
https://doi.org/10.22266/IJIES2020.0831.29 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089491901&doi=10.22266%2fIJIES2020.0831.29&partnerID=40&md5=eb171c5da699c51d12106aedd99ea9d4
Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
A uGMRT search for radio emission from planets around evolved stars
exoplanets; planets and satellites: aurorae; planets and satellites: detection; planets and satellites: gaseous planets; radio continuum: planetary systems; radio continuum: stars
In this work, we present the results from a study using the Giant Meterwave Radio Telescope (GMRT) to search for radio emission from planets around three evolved stars namely ? Tau, ? UMi, and ? Gem. Both ? Tau and ? UMi host massive ? 6 MJ mass planets at about ?1.4 au from the central star, while ? Gem is host to a 2.9 MJ mass planet at 1.7 au from the host star. We observe ? Tau and ? UMi at two upgraded GMRT bands: band 3 (250500 MHz) and band 4 (550900 MHz). We also analysed the archival observations from ? Gem at 150 MHz from GMRT. We did not detect any radio signals from these systems. At 400 MHz, the 3? upper limit is 87 ?Jy beam?1 for ? Tau b and 77.4 ?Jy beam?1 for ? UMi b. From our observations at 650 MHz, we place a 3? upper limit of 28.2 ?Jy beam?1 for ? Tau b and 33.6 ?Jy beam?1 for ? UMi b. For ? Gem b, at 150 MHz, we place an upper limit of 2.5 mJy. At 400 and 650 MHz, our observations are the deepest radio images for any exoplanetary system. The Author(s) 2024.
Narang M.; Puravankara M.; Ishwara Chandra C.H.; Banerjee B.; Tyagi H.; Tamura M.; Henning T.; Mathew B.; Lazio J.; Surya A.; Nayak P.K.
Monthly Notices of the Royal Astronomical Society, Vol-529, No. 2, pp. 1161-1168.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1093/mnras/stae536 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187227976&doi=10.1093%2fmnras%2fstae536&partnerID=40&md5=9120f146a115a4b5729a163186fdcb1b
Narang M., Academia Sinica Institute of Astronomy and Astrophysics, 11F of Astro-Math Bldg, No.1, Section 4, Roosevelt Rd, Taipei, 10617, Taiwan, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Puravankara M., Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Ishwara Chandra C.H., National Centre for Radio Astrophysics, TIFR, Post Bag 3, Ganeshkhind, Pune, 411007, India; Banerjee B., Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Tyagi H., Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Tamura M., University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, Astrobiology Center, 2-21-1, Osawa, Mitaka, Tokyo, 181-8588, Japan, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo, 181-8588, Japan; Henning T., Max-Planck-Institut f Astronomie, Kigstuhl 17, Heidelberg, D-69117, Germany; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560034, India; Lazio J., Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91106, CA, United States; Surya A., Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Nayak P.K., Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India, Institute of Astrophysics, Pontificia Universidad Catica de Chile, Av. Vicu MacKenna 4860, Santiago, 7820436, Chile
A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
Acute myeloid leukemia; Classification; Explainable AI; LIME; SHAP feature importance
Acute myeloid leukemia (AML) is a rapidly progressing disease that affects myeloid cells in blood and bone marrow. These abnormal cancerous cells called blast cells are non-functional cells that increase rapidly in bone marrow and are released into blood stream which crowd out the healthy functional cells leading to weak immune system. This life-threatening disease needs to be diagnosed at early stage and hence requires fully automated system for early detection of leukemia to aid pathologists and doctors. Most of the automated machine learning and AI models are not transparent and require techniques to explain model prediction. This paper presents methods to classify blood microscopic images into healthy or acute myeloid leukemia. Among all the methods implemented, Gradient Boosting outperforms with an accuracy of 96.67%. This paper also focuses on explainable AI to interpret model prediction and feature importance which further helps in understanding decision-making process of classification model and optimize it. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Electrical Engineering, Vol-1098, pp. 233-242.
Springer Science and Business Media Deutschland GmbH
Patil A.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Hiremath M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Pawar V., Department of Computer Applications, CMR Institute of Technology, Bengaluru, India
A Unified Approach to Two-Dimensional Brinkman-Bard Convection of Newtonian Liquids in Cylindrical and Rectangular Enclosures
BrinkmanBard convection; cylindrical and rectangular geometries; onset of convection
A unified model for the analysis of two-dimensional BrinkmanBard/RayleighBard/ DarcyBard convection in cylindrical and rectangular enclosures ((Formula presented.)) saturated by a Newtonian liquid is presented by adopting the local thermal non-equilibrium ((Formula presented.)) model for the heat transfer between fluid and solid phases. The actual thermophysical properties of water and porous media are used. The range of permissible values for all the parameters is calculated and used in the analysis. The result of the local thermal equilibrium ((Formula presented.)) model is obtained as a particular case of the (Formula presented.) model through the use of asymptotic analyses. The critical value of the Rayleigh number at which the entropy generates in the system is reported in the study. The analytical expression for the number of Bard cells formed in the system at the onset of convection as a function of the aspect ratio, (Formula presented.), and parameters appearing in the problem is obtained. For a given value of (Formula presented.) it was found that in comparison with the case of (Formula presented.), more number of cells manifest in the case of (Formula presented.). Likewise, smaller cells form in the (Formula presented.) problem when compared with the corresponding problem of (Formula presented.). In the case of (Formula presented.), fewer cells form when compared to that in the case of (Formula presented.) and (Formula presented.). The above findings are true in both (Formula presented.) and (Formula presented.). In other words, the presence of a porous medium results in the production of less entropy in the system, or a more significant number of cells represents the case of less entropy production in the system. For small and finite (Formula presented.), the appearance of the first cell differs in the (Formula presented.) and (Formula presented.) problems. 2023 by the authors.
Siddheshwar P.G.; Lakshmi K.M.; Laroze D.
Multidisciplinary Digital Publishing Institute (MDPI)
https://doi.org/10.3390/e26010002 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183090298&doi=10.3390%2fe26010002&partnerID=40&md5=67dac90c7fe10c011aefe45dcef22d04
Siddheshwar P.G., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to Be University), Hosur Road, Bengaluru, 560029, India; Lakshmi K.M., Department of Mathematics, School of Physical Sciences, Central University of Karnataka, Kalaburagi, 585367, India; Laroze D., Instituto de Alta Investigaci, Universidad de Tarapac Casilla 7D, Arica, 1000000, Chile
A unifying computational framework for fractional Gross-Pitaevskii equations
caputo derivative; gross-pitaevskii equations; laplace transform; q-homotopy analysis method
This paper concerns investigating the complex behaviour of the special case of Schringer equation called Gross-Pitaevskii (GP) equations using -homotopy analysis transform method (-HATM) with fractional order. Based on denticity function and different initial conditions, we consider three different examples to demonstrate the proficiency of -HATM. We consider different initial conditions for the hired system and the projected method is elegant unification of -homotopy analysis algorithm and Laplace transform. Further, the physical natures of the achieved results have been captured for change in space, time, homotopy parameter and fractional order in terms of contour and surface plots, and the accuracy is presented with the numerical study. The obtained results conclude that, the hired technique is highly methodical, easy to implement and accurate to examine the behaviour of the nonlinear equations of both fractional and integer order describing allied areas of science. 2021 IOP Publishing Ltd.
Veeresha P., Centre for Mathematical Needs, Department of Mathematics, CHRIST, Deemed to Be University, Bengaluru, 560029, India; Baleanu D., Department of Mathematics, Faculty of Arts and Sciences, Cankaya University, Eskisehir Yolu 29. Km, Yukariyurtcu Mahallesi Mimar Sinan Caddesi, Etimesgut, 406790, Turkey, Institute of Space Sciences, Magurele-Bucharest, Romania
A unique adventure - unity based 3D game
3D Modelling; Animations; Environment; FPS; Game
The number of gamers are increasing day by day and as a result the gaming industry has seen a huge growth. There was a curiosity to get the in-depth detail so as to how a game is developed. The final year project was a great opportunity to explore this field and to make something that would be fun as well as useful. The proposed work gives the detailed description about the entire process of game development. A game is created with three different levels. Each level comes with a particular set of objectives. The objectives of each level need to be attained in order to proceed to the next level. The environment in the game resembles the Christ Kengeri campus. For that, 3D model of Christ Kengeri campus is designed. 3D modeling is done in Unity and Blender software platform. A* is the search algorithm that has been used for pathfinding. The languages that Unity uses to operate with are objectoriented scripting languages. Scripting languages have its own syntax and the primary parts are called functions, variables and classes. Also each level has its own coding and is not linked with any other. In each level a new character is introduced. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.
Aruna S.K.; Sindhanaiselvan K.; Kumaresan M.
Journal of Advanced Research in Dynamical and Control Systems, Vol-11, No. 2 Special Issue, pp. 1953-1959.
Institute of Advanced Scientific Research, Inc.
Aruna S.K., Faculty of Engineering, Department of Computer Science and Engineering, Christ University, Bengaluru, 560074, Karnataka, India; Sindhanaiselvan K., Department of Computer Science and Engineering, MVJ College of Engineering, Near ITPB, Whitefiled, Bangalore, 560067, Karnataka, India; Kumaresan M., School of Computing Science and Engineering, Galgotias University, Plot No. 2, Yamuna Expy, Opposite, Buddha International Circuit, Sector 17A, Greater Noida, 203201, Uttar Pradesh, India
A unique model for detection of health insurance fraud while improving UX using UI based on emotion cue /
Patent Number: 201941039391, Applicant: Dr. Ilango Velchamy. The patent description consists of an inclusive model to create a better user experience. This patent primarily envisages creation of awareness among the users about the various product offerings of the companies and to detect frauds. It shall include 6 levels such as inclusion of ICT, Customer Experience, Customer journey, Emotion experience, constructing a methodology, optimization and personalization. ICT media will be used to construct a better user interface.
https://drive.google.com/file/d/1CMdAkPE_AWvOr1ncrh1svVXtgwyuOvuw/view
A unique stimulus-emotion-servicescape model for unsought goods that negates undesirability by capturing emotions and creates desirability through servicescape /
Patent Number: 201941039390, Applicant: Dr. V R Uma. Unsought goods are those products that are desired when a problem occurs or those that require aggressive selling efforts. The unsought product identified to construct the Stimulus-Servicescape model is health insurance. The clusters identified for testing the model include, the users and the non users. The non-users were further bifurcated as those that were exposed to stimuli and those that were not. Hence, there are three clusters.
https://drive.google.com/file/d/147nkaLqJvyc3qlIr0LBIUyE5xMjFHSTS/view
A USB- bluetooth two factor mutual authentication security protocol for wireless sensor networks
Bluetooth; Key agreement; Two factor mutual authentication; USB; Wireless sensor networks; WSN
Wireless sensor networks are easy to deploy, effective, and can monitor unattended environments. As the data transmitted through these networks is highly sensitive, the security of the networks is important and strong authentication measures must be in place. Authentication is done by means of a security protocol, wherein a user is authenticated through certain factors such as a smartcard or a password, and several mathematical calculations such as hashing, and XOR operations. Several previously proposed authentication protocols and their flaws are discussed in this paper. We propose a new two factor mutual authentication protocol using a USB-Bluetooth token as the second factor, to overcome the security flaws seen in previous schemes. We also provide security analysis as well as Scyther results in support of the proposed protocol. The proposed protocol can be used across various fields such as healthcare, agriculture, traffic monitoring etc. BEIESP.
International Journal of Recent Technology and Engineering, Vol-8, No. 1, pp. 1804-1810.
Blue Eyes Intelligence Engineering and Sciences Publication
Thomas P.E., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Binu S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India
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.
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 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