Explore the vast range of titles available.

Cyberbullying is the use of information technology networks by individuals’ to humiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact. Social media is the 'virtual playground' used by bullies with the upsurge of social networking sites such as Facebook, Instagram, YouTube and Twitter. It is critical to implement models and systems for automatic detection and resolution of bullying content available online as the ramifications can lead to a societal epidemic. This paper presents a deep neural model for cyberbullying detection in three different modalities of social data, namely textual, visual and info-graphic (text embedded along with an image). The all-in-one architecture, CapsNet–ConvNet, consists of a capsule network (CapsNet) deep neural network with dynamic routing for predicting the textual bullying content and a convolution neural network (ConvNet) for predicting the visual bullying content. The info-graphic content is discretized by separating text from the image using Google Lens of Google Photos app. The perceptron-based decision-level late fusion strategy for multimodal learning is used to dynamically combine the predictions of discrete modalities and output the final category as bullying or non-bullying type. Experimental evaluation is done on a mix-modal dataset which contains 10,000 comments and posts scrapped from YouTube, Instagram and Twitter. The proposed model achieves a superlative performance with the AUC–ROC of 0.98.

Automatic detection of cyberbullying in social media content is a natural language understanding and generic text classification task. The cultural diversities, country-specific trending topics hash-tags on social media, the unconventional use of typographical resources such as capitals, punctuation, emojis and easy availability of native language keyboards add to the variety and volume of user-generated content compounding the linguistic challenges. This research focuses on cyberbullying detection in the code-mix data, specifically the Hinglish, which refers to the juxtaposition of words from the Hindi and English languages. We explore the problem of cyberbullying prediction and propose MIIL-DNN, a multi-input integrative learning model based on deep neural networks. MIIL-DNN combines information from three sub-networks to detect and classify bully content in real-time code-mix data. It takes three inputs, namely English language features, Hindi language features (transliterated Hindi converted to the Hindi language) and typographic features, which are learned separately using sub-networks (capsule network for English, bi-LSTM for Hindi and MLP for typographic). These are then combined into one unified representation to be used as the input for a final regression output with linear activation. The advantage of using this model-level multi-lingual fusion is that it operates with the unique distribution of each input type without increasing the dimensionality of the input space. The robustness of the technique is validated on two datasets created by scraping data from the popular social networking sites, namely Twitter and Facebook. Experimental evaluation reveals that MIIL-DNN achieves superlative performance in terms of AUC-ROC curve on both the datasets.

Innovation diffusion models have been developed by many researchers during the past few decades based on the famous Bass (1969) model. Several such diffusion models have been developed in consideration of price, marketing efforts etc., however, it is hardly seen that customer attrition (disadoption) can play a significant role in long term growth process of any new product or service. This paper defines two types of disadoption process, Type I disadoption and Type II disadoption process, representing disadopters from innovators and imitators, respectively. We illustrate that there is an increase in the market size along with the adoption of new product and this increase is addressed in this paper. The explicit mean value function for the two types of disadoption processes is derived in this paper. The thrust of the research is on studying the management educational services in the Delhi/NCR region of India and the impact of disadoption on the long term growth of such services. In order to validate the proposed modeling framework, we make use of different goodness-of-fit criteria on primary data collected from an institute in Delhi/NCR.

As a constructive mode of information sharing, collaboration and communication, social media platforms offer users with limitless opportunities. The same hypermedia can be transposed into a synthetic and toxic milieu that provides an anonymous, destructive pedestal for online bullying and harassment. Automatic cyberbullying detection on social media using synthetic or real-world datasets is one of a proverbial natural language processing problem. Analyzing a given text requires capturing the existent semantics, syntactic and spatial relationships. Learning representative features automatically using deep learning models efficiently captures the contextual semantics and word order arrangement to build robust and superlative predictive models. This work puts forward a hybrid model, Bi-GRU-Attention-CapsNet (Bi-GAC), that benefits by learning sequential semantic representations and spatial location information using a Bi-GRU with self-attention followed by CapsNet for cyberbullying detection in the textual content of social media. The proposed Bi-GAC model is evaluated for performance using F1-score and ROC-AUC curve as metrics. The results show a superior performance to the existing techniques on the benchmark Formspring.me and MySpace datasets. In comparison to the conventional models, an improvement of nearly 9% and 3% in F-score is observed for MySpace and Formspring.me dataset respectively.

Cyberbullying is to bully someone in the digital realm. It has become extremely detrimental as the social media and the internet have become more popular and omnipresent. People use the internet services to viciously attack others from behind a screen. The substantial growth in the dimensionality, heterogeneity, subjectivity and multimodality of social media and the pressing need to timely curtail the damage instigated through cyberbullying, has fostered the need to devise automated mechanisms which detect such unfavorable activities. The use of soft computing techniques to handle such pernicious issue has been studied invariably and widely in literature. This study is to understand the viability, scope and significance of this alliance of using soft computing techniques for cyberbullying detection on social multimedia. This work is a systematic literature review to gather, explore, comprehend and analyze the research trends, gaps and prospects of this pairing in a well-organized way. The contribution of this study is noteworthy as it focuses on the use and application of soft computing techniques for cyberbullying detection on social multimedia utilizing a meta-analytic approach in order to integrate, interpret and critically analyze the findings in the original studies for expounding novel approaches to achieve comparable and effectual results pertaining to the defined research domain. Published studies starting April 2003, accessed from six digital portals (ACM, IEEE, Elsevier, Wiley, Springer and Taylor and Francis) have been reviewed to expound the state-of-art within the domain to give insightsand finally identify the directions of future research.

Currently, the expansion of VLSI industry is primarily focussed on the way to the efficiency of semiconductor devices which in turn is extremely dependent on the advancement in the CMOS technology. As the scaling down of device dimensions are being aggressive, gate tunnelling effect, p-n junction leakage current increases, and sub-threshold slope increases. More precise and novel device structures are required to be developed for overcoming the above mentioned problems. These needs have led to the development of alternative technology. So SOI technology has been invented with a buried oxide layer in the silicon substrate. Due to the isolation created by this buried oxide in the substrate, various short channel effects have been reduced. This paper presents an electrical comparison between a 45 nm n-channel Metal Oxide Semiconductor Field Effect Transistor (NMOSFET) and 45 nm Silicon On Insulator (SOI) MOSFET simulated using SILVACO ATLAS simulator. The fabrication of both the devices have been carried out in SILVACO TCAD software and estimation of threshold voltage, drain current, and sub-threshold slope has been done. Drain current versus gate voltage and drain current versus drain voltage curves have been plotted and compared. By comparing the characteristics of the bulk and SOI MOSFET, the SOI MOSFET has been found to be better than the bulk NMOSFET.

PurposeAlmost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues.Design/methodology/approachThe authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy.FindingsFrom the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance.Originality/valueThe authors are proposing a generalized model for software scheduling.

This paper explores the impact of lightly doped (LD) and heavily doped (HD) substrates ona Metal Oxide Semiconductor Field Effect Transistor (MOSFET) with 40nm Gate length. The influence of varying the p-type substrate doping 15 18 -3 concentration (from 10 to 10 cm ) is investigated in terms of the drain current, substrate current, sub-threshold current, on-off current ratio, sub-threshold swing and threshold voltage. The simulation results show that the lightly doped substrate devices with high work-function gives improvedoff state leakage current. It has also been observed that LD devices have high drain current even on low gate oxide thickness. All the simulation & design work has been done in SILVACO TCAD software.

In this paper, the concentration of halo implant and threshold implant has been varied to estimate the sub-threshold leakage current and substrate current of the MOSFET. A lightly doped NMOS has been designed having channel length of 40 nm in Athena and simulated in Atlas of Silvaco TCAD tool. After simulation results, it has been observed that as the threshold implant and halo implant concentrations are increased, there is a decrease in both off-state sub-threshold leakage and substrate current as required for an ideal MOSFET. Other parameters like ON current, DIBL, and threshold voltage have also been estimated.

Purpose The problem of evaluating potential suppliers has always been based on finding an optimal tradeoff between supplier’s performance consistently meeting firms’ needs and acceptable cost. The purpose of this paper is to propose a hybrid multi-criteria decision framework to quantify this qualitative judgment and reduce ambiguity in selection of suppliers in the era of Industry 4.0. Design/methodology/approach A hybrid intuitionistic fuzzy entropy weight-based multi-criteria decision model with TOPSIS is proposed. The authors make use of the intuitionistic fuzzy weighted approach operator for aggregating individual decision maker’s opinions regarding each alternative over every criterion. Additionally, the authors employ the concept of Shannon’s entropy to calculate the criteria weights. Findings Results obtained on the basis of the proposed hybrid methodology are analyzed against two more cases wherein the authors try to showcase the relevance of using IFS and entropy-based decision framework and find out the uniqueness of the proposed framework in supplier selection process. Practical implications The proposed model is apposite to solve management problem of supplier selection in two ways: aggregating individual decision maker’s opinion for each of the predefined criteria along with individual decision maker’s importance and ranking the suppliers based on both positive and negative ideal solutions using TOPSIS. Originality/value A robust framework incorporates not only suppliers’ performance but also provides weightage to key decision makers. Especially in the context of MCDMs wherein both qualitative and quantitative data is evaluated simultaneously, the proposed framework is unique in its practical implementation of reducing ambiguity in the supplier selection process.