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Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Detecting the magnitude of depression in Twitter users using sentiment analysis
Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved. -
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE. -
Desymmetrisation of meso-2,4-Dimethyl-8-oxabicyclo[3.2.1]-oct-6-ene-3-ol and its Application in Natural Product Syntheses
The compounds containing chiral centers and different functional groups serve as magnificent building blocks for the preparation of various natural products that are having immense biological activity. Dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol is one of the wonderful synthons to construct multiple stereo centers at a time during the asymmetric synthesis. In this account, we discuss our research efforts toward the synthesis of various simple and complex natural products from the past three decades (19952020) by using dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol as a synthon. Moreover, the synthetic utility of this starting material was investigated and well demonstrated. Further, we executed the desymmetrization of dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol by hydroboration to get different chiral centers. After obtaining the stereocenters, we could manage either the fragment, formal or total synthesis of natural products, by simple protection and deprotection sequence followed by C?C bond formation steps. 2021 The Chemical Society of Japan & Wiley-VCH GmbH -
Destination Resilience and Smart Tourism Ecosystem : A Destination Management Framework for Competitiveness
Over the past many decades, the travel and tourism industry has been at the forefront of adapting to new changes and accepting the latest technologies. Today's travelers are sophisticated and knowledgeable, as they have all the information available to them easily, which contributes to fast and quick decision making. The world is gradually changing into a much more intelligent and advanced platform that makes it possible to employ techniques like augmented reality, virtual reality, and artificial intelligence. This has proven to be very successful in a variety of fields, including education, healthcare, marketing, and communication. The current study focuses on incorporating smart tourism strategies to build a sustainable ecosystem at destinations, which enhances the competitiveness of the destination and makes it easier for value co- creation among the different stakeholders. Research suggests that although industry-led and government-initiated projects seem to prioritize the use of smart applications in destinations in theory, practical implementation appears to lag behind. Less research has been done in India on gamification, smart wearable technology at travel destinations, and the practical application of AR and VR tools. The study revolves around the South Indian State of Kerala, which has been a pioneer in tourism promotion in the country. In addition to proposing a framework for destination management and tourism competitiveness with smart tourism applications, this study aims to investigate the practical implications of smart tourism tools and technologies at destinations. To shed more light on the findings, a mixed methodology approach is used to analyze the data using a mix of quantitative and qualitative methods. The study's conclusions have significant ramifications for destination management, strategic planning, and the application of smart technologies at travel locations. -
Destination image and perceived meaningfulness for visitor loyalty: A strategic positioning of Indian destinations
The purpose of this study is to empirically test and validate a multi-dimensional structure of In-loco Destination Image and perceived meaningfulness using an integrated model of visitor loyalty. The model was tested using data collected from responses of foreign tourists visiting India (n = 246). The results identified six dimensions of In-loco Destination Image: Amenities, Attractions, Leisure, Culture, Support Systems, and Hospitality. In addition, the investigation observes that of the identified dimensions of perceived meaningfulness, the spiritual and societal dimensions contribute more to perceived meaningfulness than the physical well-being aspect. Further, the exploration estimated the theoretical framework developed using structural equation modelling and established the mediating role of perceived meaningfulness in developing visitor loyalty from In-loco Destination Image. The studys observations helped identify three positioning approaches, namely objective, subjective, and combined, offering suggestions to destination marketers to effectively reposition Indian destinations. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Destination governance and a strategic approach to crisis management in tourism /
Journal Of Investment And Management, Vol.5, Issue 1, pp.1-5, ISSN: 2328-7721 (Online), 2328-7713 (Print). -
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Despeckling of Ultra sound Images using spatial filters - A Fusion Approach
Ultra sound images are normally affected by speckle noise which is typically multiplicative in nature. This study proposes different fusion based despeckling methods for ultra sound images. The output of existing spatial domain despeckling methods viz. Lee filter, Bayesian Non Local Means (BNLM) filter and Frost filter are fused pairwise. Fusion is implemented in two steps, first an inter-scale stationary wavelet coefficient fusion followed by an intra-scale wavelet coefficient fusion. Analysis of these projected despeckling strategies are conducted using metrics like Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI). The results show that the performance of fusion based methods is better than the respective individual filters for despeckling ultra sound images. 2019 IEEE. -
Desk organizer /
Patent Number: 350152-001, Applicant: Vaibhav Tripathi. -
Desk organizer /
Patent Number: 350152-001, Applicant: Vaibhav Tripathi. -
Desk organizer /
Patent Number: 350152-001, Applicant: Vaibhav Tripathi. -
Desk organizer /
Patent Number: 350152-001, Applicant: Vaibhav Tripathi. -
Desiri Naturals: sustainable agriculture and eco-friendly business
Learning outcomes: After completion of the case study, the students will be able to critically analyze the business model of Desiri Naturals, analyze the pricing strategy of Desiri Naturals, examine the importance of experiential marketing in the success of an environment-friendly business, identify the challenges faced by new entrepreneurs and evaluate the sustainability practices of Desiri Naturals. Case overview/synopsis: This case study discusses the business model of an environmentally friendly business. The challenges and obstacles faced by entrepreneurs are illustrated in this case. The entrepreneurs vision to provide chemical-free food is highlighted and their business operations as a means to fulfill this vision are explained. Desiri used an age-old bull-driven method of oil extraction (Ghana). Challenges in pricing due to the availability of low-priced mass-produced edible oil using the solvent extraction process are presented in this case. The entrepreneurs faced the pricing dilemma at the inception of the business, as oil produced using the natural cold pressing method cost three times the selling pricing of solvent-extracted oil. Innovative methods of experiential marketing such as Ghana tourism are explained in this case. This case study also explains the sustainable and natural farming techniques propagated through its network of farmers. This case study provides insights into the scalability of this model and the scope for employment generation in rural India. The environmentally friendly practices followed by Desiri, such as the use of glass bottles and reusable steel containers for packaging oil are emphasized. Finally, this case presents the marketing and operational challenges faced by entrepreneurs in their quest to expand their operations. Complexity academic level: This case study can be used by postgraduate and undergraduate students studying marketing, entrepreneurship, sustainability and operations management courses in commerce and business management streams. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS8: Marketing. 2024, Emerald Publishing Limited. -
Designing social learning analytics for collaborative learning using virtual reality, life skill, and STEM approach
This chapter explores the design of social learning analytics for collaborative learning, incorporating virtual reality, life skills, and a STEM approach. Researchers employ social learning analytics, an emerging field that combines social network analysis and learning analytics, to gain insights into collaborative learning environments. The chapter emphasizes integrating virtual reality, life skills, and STEM in social learning analytics, covering data collection methods, data analysis techniques, and pedagogical applications. It also explores key considerations for designing social learning analytics in collaborative learning, encompassing the development of tools and assessment strategies. Finally, the chapter looks ahead to future directions and prospects for social learning analytics in collaborative learning. 2024, IGI Global. All rights reserved. -
Designing of a Free-Standing Flexible Symmetric Electrode Material for Capacitive Deionization and Solid-State Supercapacitors
In this work, a highly efficient free-standing flexible electrode material for capacitive deionization and supercapacitors was reported. The reported porous carbon shows a high surface area of 2070.4 m2 g-1 with a pore volume of 0.8208 cm3 g-1. The material exhibited a high specific capacitance of 357 F g-1 at 1 A g-1 in a two-electrode symmetric setup. A solid-state supercapacitor device has been fabricated with a total cell capacitance of 152.5 F g-1 at 1 A g-1 in a solid PVA/H2SO4 gel electrolyte with an energy density of 21.18 W h kg-1 at a 501.63 W kg-1power density. A long-run stability test was carried out up to 15,000 cycles at 5 A g-1 that showed capacitance retention of 99% with ?100% Coulombic efficiency. Furthermore, the electrosorption experiment was conducted by a flow-through test by coating on commercially available cellulose thread that was employed, which shows electrosorption ability up to 16.5 mg g-1 at 1.2 V in a 500 mg L-1 NaCl solution. Complete experiments were conducted with a proper procedure, provided by scientific approaches with analytical data. Thus, the reported electrode material showed bifunctional application for energy storage and environmental remediation. 2023 American Chemical Society. -
Designing coordinatively unsaturated metal sites in bimetallic organic frameworks for oxygen evolution reaction
Metal organic frameworks (MOFs) are developing as promising catalysts for oxygen evolution reactions. A bimetallic electrocatalyst MOF using Ni and Cu as metal sources and 1,4-benzene dicarboxylic acid as a linker has been synthesized and evaluated for oxygen evolution reaction. Compared to monometallic MOFs, bimetallic MOFs participate more actively in electrocatalysis due to the higher abundance of active sites, local crystallinity, and lower long-range disorder. When utilized as oxygen evolution catalysts, NiCu MOFs have a low overpotential of 340 mV at 10 mA/cm2 and a low Tafel slope of 65 mV/dec. The study paves the way for the development of highly efficient catalysts for water splitting applications. 2023 Elsevier Ltd