Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.

Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users’ textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language.

The rapid and revolutionary advancement in technology has increased the demand for advanced ubiquitous mobile applications in our daily lives. Home automation system uses collective smart technology to elevate sphere of security and human living standards. Wireless Smart home systems have been prototyped by researchers to realize different applications in vast contexts. In this paper we propose a keyless smart home security system which uses Bluetooth as a communication protocol to automate door locks based on a security code and predefined user credentials. The system combines Android phone as a signal sender to dispatch the input signal from an end users side, Bluetooth communication module as signal receiver, ARDUINO microcontroller as CPU and servo motor and light emitting diodes as an output. The system integrates two modules: one of which is Arduino microcontroller which is responsible for authentication and verification of security code and allows servo motor to process the output according to the received input. The second is android Application which provides an interface to the user to set credentials and security code which is then transmitted to the Arduino microcontroller via Bluetooth technology. Since Arduino and Android are open source, hence implementation rate of the proposed system is inexpensive and feasible.

Speech isa zestful, and intricate activity that enables people to express ideas, emotions, and thoughts. We are able to render our views because of this neural activity. It is a significant process for learning and personal development that every individual deserves to develop, including those with special needs and who are on a journey to learn how to communicate. Children frequently suffer from speech disorders. This entails them at the risk of experiencing social, intellectual, and academic challenges that may persist and affect them in their adolescence and adulthood. In this context, we present a speech therapy solution for special children, to assist kids with speech and language impairments in improving their language skills. The proposed app can act as a useful management tool and rehabilitation system for people with aphasia disorder and their caretakers including parents, guardians, and teachers. This innovative app offers a vast number of features and practice sessions to develop language skills and overcome communication impairment problems. It also supports multiple regional languages, including English, Urdu, and Sindhi allowing users to switch between native languages effortlessly through the settings. The developed app is equipped with a dynamic accuracy assessment, and progress-tracking system, notifying the parents or guardians when practice sessions are missed, ensuring that language development remains consistent and effective. The major novelty of this work is that it has considered regional aphasia patients and their language needs. In contrast to the existing developed therapeutic tools which are mainly oriented towards resource-rich languages, the proposed work aims to address regional languages. The proposed speech therapist App for children can be a powerful tool for parents, caregivers, and educators, providing a fun and interactive way for children to improve their speech and language abilities. The developed solution also offers benefits in the context of enhanced patient involvement, motivation throughout their learning journey, greater flexibility, and accessibility in contrast to in-person therapy, immediate feedback, and careful progress monitoring that makes it easier to assess and modify treatment sessions.