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Diabetes is one of the ever-increasing menace crippling millions of people worldwide. It is an independent risk factor for many cardiovascular diseases including medium and small vessels and results in heart attack, stroke, kidney failure, blindness, and lower-limb amputations. According to a World Health Organization (WHO) report estimated 1.6 million deaths were the direct result of diabetes. Nutrition plays a vital role in diabetes management alongside physical activity, drugs, and insulin. Weight management can help to avert or delay at pre-diabetic stages. This research work explains the features of the Nutrition Diet Expert System (NDES), which will preferably be used by the health care professionals (HCPs) for calculating per day calorie requirements of diabetic patients and recommend the best diet plan to control diabetes. The primary objective of this proposed model for diet plan is to help individuals attain healthy body weight and optimum check on diabetes by gaining control over blood pressure and lipid count. The ultimate focus of this research result is prevention of diabetes related complications using nutrition diet expert system. In this paper, proposed recommender system has come in handy in figuring out the diet plan by determining the individual dietary requirements at the level of micro and macro nutrients for expert system using fuzzy logic. The results are very promising indicating testing and assessment of the expert system worked well for the individual diet plan. [ABSTRACT FROM AUTHOR]

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset. [ABSTRACT FROM AUTHOR]

The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the internet with updated controls. In such systems, the task of transferring games and video compression imposes huge computational complexity is required on cloud servers. The basic problems in cloud gaming in particular are high encoding time, latency, and low frame rates which require a new methodology for a better solution. To improve the bandwidth issue in cloud games, the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay. In this paper, the proposed improved methodology is used for automatic unnecessary scene detection, scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene. As a result, simulations showed without much impact on the players' quality experience, the selective object encoding method and object adaption technique decrease the network latency issue, reduce the game streaming bitrate at a remarkable scale on different games. The proposed algorithm was evaluated for three video game scenes. In this paper, achieved 14.6% decrease in encoding and 45.6% decrease in bit rate for the first video game scene. [ABSTRACT FROM AUTHOR]