Browse Items (9795 total)
Sort by:
-
A unifying computational framework for fractional Gross-Pitaevskii equations
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. -
A unique adventure - unity based 3D 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. -
A USB- bluetooth two factor mutual authentication security protocol for wireless sensor networks
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. -
A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants
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. -
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. -
A Video Surveillance-based Enhanced Collision Prevention and Safety System
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. -
A Voting Enabled Predictive Approach for Hate Speech Detection
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. -
A Way Towards Next-Gen Networking System for the Development of 6G Communication System
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. -
A weighted-Weibull distribution: Properties and applications
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. -
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, 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 -
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]
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. -
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
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. -
Acacia auriculiformisDerived Bimodal Porous Nanocarbons via Self-Activation for High-Performance 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. -
Academic Certificate Validation Using Blockchain Technology
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. -
Academic stress and its sources among university students
Stress has become part of students' academic life due to the various internal and external expectations placed upon their shoulders. Adolescents are particularly vulnerable to the problems associated with academic stress as transitions occur at an individual and social level. It therefore, becomes imperative to understand the sources and impact of academic stress in order to derive adequate and efficient intervention strategies. The study employed a quantitative research design where participants were screened using Academic Stress Scale (Rajendran& Kaliappan,1991 from four streams namely, commerce, management, humanities, and basic sciences. The five dimensions of sources such as personal inadequacy, fear of failure, interpersonal difficulties with teachers, teacher pupil relationship and inadequate study facilities were further analysed and gender differences were also obtained. Understanding the sources of stress would facilitate the development of effective counselling modules and intervention strategies by school psychologists and counsellors in order to help students alleviate stress. Published by Oriental Scientific Publishing Company 2018. -
Academic workbench for streetlight powered by solar PV system using internet of everything (IoE)
Renewable energy is one of the growing trend in developing countries. Rapid development of renewable energy leads to the economic benefits and reduce environmental pollution. According to current scenario 20 to 40 percent of the power generated is consumed by streetlights. The problems faced by the current street lighting systems are when there is availability of light there is no proper utilization. Sun intensity shift is not constant all the time, it varies as the climate changes. Real time monitoring and control using intelligent algorithm avoids energy wastage during day time. ZigBee as a communication protocol current and voltage values are sent and received. Base Controller (Single Board Computer) acts as an interphase between the communication protocol and the cloud account. Remote client application is developed to control and monitor streetlight. 2018 IEEE. -
Acceptance of consumer-oriented health information technologies (chits): Integrating technology acceptance model with perceived risk
This paper is focused on understanding the growing demand for consumer-oriented health information technologies (CHITs) wearable and adult healthcare management apps. This study utilised the Technology Acceptance Model (TAM) and integrated the concept of perceived risk. The structural Equation Modelling (SEM) technique was applied to test the research hypotheses based on the 450 quantitative responses. This study confirms significant relationships between perceived usefulness, perceived ease of use, perceived risk, attitude, behavioural intention, and actual intention in using CHITs. The findings also showed no evidence to conclude that age and education influenced respondents perceived usefulness and perceived ease of the CHITs. This study incorporated the perceived risk to fill a gap in the literature and broaden the current TAM theoretical application in the public health setting. The study findings fill the health-related technology acceptance literature gap and broaden TAM's present application in the public health realm. 2021 Slovene Society Informatika. All rights reserved. -
Accessing Accurate Documents by Mining Auxiliary Document Information
Earlier techniques of text mining included algorithms like k-means, Nae Bayes, SVM which classify and cluster the text document for mining relevant information about the documents. The need for improving the mining techniques has us searching for techniques using the available algorithms. This paper proposes one technique which uses the auxiliary information that is present inside the text documents to improve the mining. This auxiliary information can be a description to the content. This information can be either useful or completely useless for mining. The user should assess the worth of the auxiliary information before considering this technique for text mining. In this paper, a combination of classical clustering algorithms is used to mine the datasets. The algorithm runs in two stages which carry out mining at different levels of abstraction. The clustered documents would then be classified based on the necessary groups. The proposed technique is aimed at improved results of document clustering. 2015 IEEE. -
Accessing the role of critical success factors for successful ERP implementation at Indian SMEs: A statistical validation
Indian SMEs are also integral part of Indian economy; they also face numerous challenges in implementing technologies such as enterprise resource planning (ERP) systems, including a lack of human, technical and financial resources to support such initiatives. Like many other technological advances, ERP systems were initially implemented mostly at large organisations even in India. Their relative absence from Indian SMEs has probably been the main reason for the research focus on large Indian enterprise. A model is developed with the help of quantitative survey-based method to identify and rank the 30 CSFs and, then a framework has been proposed in terms of recommendations for managing these CSFs. It was determined whether the survey instrument was complete and clear or not with the help of pre-pilot survey of 30 questionnaires responses from the Indian ERP consultants. As a result, the initial survey instrument was extensively revised. For the final data collection, new revised survey instruments were then given via a survey to 500+ Indian ERP consultants. Copyright 2013 Inderscience Enterprises Ltd. -
Accident Detection Using Convolutional Neural Networks
Accidents have been a major cause of deaths in India. More than 80% of accident-related deaths occur not due to the accident itself but the lack of timely help reaching the accident victims. In highways where the traffic is really light and fast-paced an accident victim could be left unattended for a long time. The intent is to create a system which would detect an accident based on the live feed of video from a CCTV camera installed on a highway. The idea is to take each frame of a video and run it through a deep learning convolution neural network model which has been trained to classify frames of a video into accident or non-accident. Convolutional Neural Networks has proven to be a fast and accurate approach to classify images. CNN based image classifiers have given accuracy's of more than 95% for comparatively smaller datasets and require less preprocessing as compared to other image classifying algorithms. 2019 IEEE.