Knowledge discovery helps mitigate the shortcomings of classical machine learning, especially those so-called imbalanced, high-dimensional, and noisy data challenges. Adaptive combination of multiple models, voting and other data fusion strategies, and the incorporation of other disparate information fusion methods characterize ensemble learning, which addresses the improvement of a predictive model’s accuracy, stability, and generalization. This paper provides a summary of the important approaches to ensemble learning and their real-world uses, emphasizing challenges and opportunities for future work. This paper also discusses how ensemble learning integrates with emergent areas such as deep learning and reinforcement learning. This paper also describes the most important machine learning methods for predicting heart disease, which include decision trees, support vector machines, artificial neural networks, Naïve Bayes, random forest, and K-nearest neighbors.

Vehicle to Vehicle (V2V) communication plays a significant role in the Intelligent Transportation System (ITS) in Vehicular Ad hoc Networks (VANET) for which the usage of IOT in vehicles is increasing rapidly. Vehicles communicate with each other through wireless networks. However, the deployment of new generation of mobile networks 5G needs a major upgradation of its existing systems such as 4G, LTE and other infrastructure. Therefore, it is proposed to introduce advanced technology of 5G networks upgradation in Vehicle to Vehicle communication. Massive MIMO have the important role for the DSRC (Dedicated Short Range Communication) wireless technology. This mechanism works on the vehicle-to-vehicle communication such as the vehicle relative speed, range transmission etc., base station (tower) and RSU control and monitor of the vehicle to vehicle communication. In this research, the road styles such as square, straight, triangle and any other are designed and tested through simulation program using MATLAB 2017. The upcoming 5G technology for driverless V2V communication makes the journey easier and safer with full control.

Document Image Understanding (DIU) and Electronic Document Management are active fields of research involving image understanding, interpretation, efficient handling, and routing of documents as well as their retrieval. Research on most of the noncursive scripts (Latin) has matured, whereas research on the cursive (connected) scripts is still moving toward perfection. Many researchers are currently working on the cursive scripts (Arabic and other scripts adopting it) around the world so that the difficulties and challenges in document understanding and handling of these scripts can be overcome. Sindhi script has the largest extension of the original Arabic alphabet among languages adopting the Arabic script; it contains 52 characters, compared to 28 characters in the original Arabic alphabet, in order to accommodate more sounds for the language. There are 24 differentiating characters with some possessing four dots. For Sindhi OCR research and development, a database is needed for training and testing of Sindhi text images. We have developed a large database containing over 4 billion words and 15 billion characters in 150 various fonts in four font weights and four styles. The database contents were collected from various sources including websites, books, and theses. A custom-built application was also developed to create a text image from a text document that supports various fonts and sizes. The database considers words, characters, characters with spaces, and lines. The database is freely available as a partial or full database by sending an email to one of the authors.

This article presents a novel architecture using a hybrid model for developing a Sindhi spellchecker system which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language presents a substantial challenge for developing a Sindhi spellchecker system and generating a similar suggestion list for misspelled words. In order to implement such a system, phonetic-based Sindhi language rules and patterns must be taken into account for increasing the accuracy and efficiency. In this research work, a simple and efficient combinational hybrid system is proposed, using three different algorithms, the Edit Distance algorithm to find the measure of similarity between two Sindhi strings. The phonetic-based SoundEx and ShapeEx algorithms are developed for pattern or glyph matching, generating accurate and an efficient suggestion list for incorrect or misspelled Sindhi words. The proposed system is established with a blend between Phonetic-based SoundEx algorithm and ShapeEx algorithm for pattern or glyph matching, generating accurate and efficient suggestion list for incorrect or misspelled Sindhi words. In this article, a table of phonetically similar-sounding Sindhi characters is presented which are grouped together along with another table containing similar glyph or shape-based character groups. The system has been successfully integrated into a pre-developed Sindhi word processer application. The Sindhi word segmentation methodology and algorithms required for the spellchecker has already been published and so are not discussed in detail in this article.

Sindhi is one of the historical languages which is widely used in all over the world, but especially in the province of Sindh Pakistan. Sindhi language has its own script and written by the right-handed. Nowadays the use of different Sindhi platforms is increasing especially for communication. The majority of the people of Sindh province read, write and speak very well, but they face the problem in text communication while using different communication platforms. However, the users of computer and mobile phone feel trouble/difficulty during the use of the Sindhi script in typing of text messages, tweets and comments while using different platforms in computer and mobile phone. Natural Language Processing (NLP) is one of the better options for the solution of these problems of text communication on different platforms. For the proper solution of text communication issues, Romanized Sindhi text is used instead of Sindhi text. Romanized text writing is easier than the Sindhi text writing because Sindhi text writing needs the special type of keyboard while writing of Romanized text does not need any special type of keyboard. For the writing of Romanized Sindhi text, rules are defined in this paper which provide easiness during writing and understanding of the text. Romanized Sindhi Rules (RSR) are simple and easy to understand the meaning of the text and provide fast communication (text). This study is also helpful for further research in the Romanized Sindhi text by using different approaches and provides easiness in communication.

Sentiment Analysis have also an important role in natural language processing to evaluate and analyzing the public opinion, sentiments and views about social activities such as product, services, Academic institutes, organizations etc. Lot of work has been done on English language in natural language processing. However, it is found out from the literature that still huge research gap is available for the Romanized Sindhi and there sentiment analysis in the field of natural language processing and also no any trained data is available for the testing. Classification of sentiment of Romanized Sindhi text is very difficult task. For the evaluation of sentiment of Romanized Sindhi text easily available online Python tool were used. In this research work thousand words of Romanized Sindhi text/data were used for the sentiment classification. Also discussed issues in sentiment classification in Python tool on Romanized Sindhi text.