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Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

Genetic Algorithm often bears with Premature Convergence for solving combinatorial optimization problems but can be improved by modification at different prospectives. In this research, an effective knowledge is applied in the procedure of Genetic Algorithm used for solving TSP. The key concept for the proposed GA is a modification in the crossover operators by applying knowledge of smallest distant cities (shortest edge) assuming that it would improve the process to find the shortest path, by optimizing the new generations. As TSP is represented as a graph, considering cities as nodes and paths as edges, in the proposed approach, crossover point selection for crossover operators is selected upon the basis of minimum edge in the given graph. To test the effect of the proposed method of knowledge application, Linear Order Crossover, Cycle Crossover Operator, and Sequential Constructive Crossover are modified and results are proved on the randomly generated data sets for TSP. This optimization not only improved the shortest path but also reduced the problem of Premature Convergence.

In the futuristic Industry framework, user interactions with the product are seamlessly integrated with the product life cycle which results in Information overload. The shopbots were proposed in Industry 4.0 where the more focus was on process automation. In this research work, we propose recommender system for industry 5.0 which is based on collaboration of human beings and machines with more focus on user’s personalization. Recommender system can be considered as an information filtering tool that provides suggestions to users about products, music, friend, topic, etc. This suggestion is based on the interest of users. Several research works have been carried out to improve recommendation accuracy by using matrix factorization, trust-based, hybrid-based, machine learning, and deep learning techniques. However, very few existing works have leveraged textual opinions for the recommendation to the best of our knowledge. Existing research works have focused only on numerical ratings, which do not reflect actual user behaviour. In this research work, sentiments of textual opinions are analysed for an in-depth analysis of user’s behaviour. Recommendation accuracy is improved by using the proposed score Recop which is calculated from opinion sentiments. Furthermore, the sparsity issue is resolved by using our proposed approach. Amazon and Yelp review datasets are used for Experiment analysis. Mean absolute error (MAE) and root mean square error (RMSE) values are improved significantly using the proposed approach compared to the existing approaches. MAE and RMSE scores on the Yelp dataset are 0.85 and 1.51, respectively. Additionally, MAE and RMSE scores on the Amazon dataset are 0.66 and 0.93, respectively, which clearly reflects the significant contribution of our proposed approach.

Cloud Computing is an eminent and reputable agenda which relies on large-scale distributed processing to provide access to their resources and services. In the cloud environment a rigorous management system is mandatory to collect all information regarding task processing levels and proving impartial resource provisioning through the levels of Quality of Service (QoS). These concerns can be settled by employing a meta-heuristic optimization-based resource management. Subsequently, this paper presents a Fuzzy Emperor Penguin Optimization (Fuzzy-EPO) algorithm-based resource provisioning framework for heterogeneous cloud environment. To deploy the optimal set of virtual machines (VM) to physical machines the VM allocation model is employed. The proposed Fuzzy-EPO algorithm does the VM consolidation mainly to reallocate overloaded VM to under-loaded PM to minimize the migration time and the brownout mechanism is adopted to reduce the rate of energy consumption. CloudSim simulation platform is used to implement the proposed system. The simulation results expose that the proposed Fuzzy -EPO based system is effective in restraining the proportion of SLA violation and increasing QoS requirements for providing proficient cloud service

Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

Social Big Data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in social Big Data. If any user intends to select products such as movies, books, etc., from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity, but not efficient for scalability. The main focus of this article is to improve scalability in terms of locality and throughput and provides better recommendations to users with large-scale data in less response time. In this article, the social big graph is partitioned and distributed on different nodes based on Pregel and Giraph. In the proposed approach ScaleRec, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using proposed approach. In ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets such as Epinions and LiveJournal proves that better locality and recommendation accuracy is achieved by using ScaleRec.

Deep learning is the most active research topic amongst data scientists and analysts these days. It is because deep learning has provided very high accuracy in various domains such as speech recognition, image processing and natural language processing. Researchers are actively working to deploy deep learning on information retrieval. Due to large-scale data generated by social media and sensor networks, it is quite difficult to train unstructured and highly complex data. Recommender system is intelligent information filtering technique which assists the user to find topic of interest within complex overloaded information. In this paper, our motive is to improve recommendation accuracy for large-scale heterogeneous complex data by integrating deep learning architecture. In our proposed approach ratings, direct and indirect trust values are fed in neural network using shared layer in autoencoder. Comprehensive experiment analysis on three public datasets proves that RMSE and MAE are improved significantly by using our proposed approach.

In the futuristic Industry framework, user interactions with the product are seamlessly integrated with the product life cycle. A recommender system can be considered as an information filtering tool that provides suggestions to users about products, music, friend, topic, etc. This suggestion is based on the interest of users. Several research works have been carried out to improve recommendation accuracy by using matrix factorization, trust-based, hybrid-based, machine learning, and deep learning techniques. However, very few existing works have leveraged textual opinions for the recommendation to the best of our knowledge. Existing research works have focused only on numerical ratings, which do not reflect actual user behaviour. In this research work, sentiments of textual opinions are analyzed for an in-depth analysis of users' behaviour. Furthermore, Natural Language Processing techniques such as lemmatization, stemming, stop-word removal, Part-of-Speech (POS) tagging are applied to textual opinions. Recommendation accuracy is improved by using the proposed score Recop calculated from opinion sentiments. Furthermore, the sparsity issue is resolved by using our proposed approach. Amazon and Yelp review datasets are used for Experiment analysis. Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) values are improved significantly using the proposed approach compared to the existing approaches. MAE and RMSE scores on the Yelp dataset are 0.85 and 1.51, respectively. Additionally, MAE and RMSE scores on the Amazon dataset are 0.66 and 0.93, respectively, significantly contributing to our proposed approach.

Opinions for any product, topic or organization are provided by users on Social networking and E-commerce sites. These opinions have high influence on other users’ purchasing decisions. Spammers post fake opinions so that their organization’s product can be promoted and competitor’s product can be demoted. There is need for detecting spam opinions so that users can have fair information about any product. Review text and reviewer behavior are factors considered to detect spam opinions. Traditional machine learning techniques such as SVM, Logistic Regression and Naive Bayes are applied to distinguish spam opinions from original reviews, but accuracy is not adequate. Furthermore, traditional approaches use only bag-of-words representation which do not consider context of words. In this research work, reviews text are observed and fed into Convolutional Neural Network (CNN) model. Convolutional layers, number of hidden layers, dense layers and filter size are set to build model. Further, essential text features are included in CNN model to improve accuracy. GloVe word embedding is used in proposed model. Experiment analysis on Ott dataset proves that accuracy is improved significantly by proposed model as compared to traditional machine learning approaches.

Recommender system is an data retrieval system that gives customers the recommendations for the items that a customer may be willing to have. It helps in making the search easy by sorting the huge amount of data. We have progressed from the era of paucity to the new era of plethora due to which there is lot of development in the recommender system. In today’s scenario the interaction between the groups of friends, family or colleagues has increased due to the advancement in mobile devices and the social media. So, group recommendation has become a necessity in all kinds of domains. In this paper a system has been proposed using the group recommendation system based on hybrid based filtering method to overcome the cold start user issue which arises when a new user signs in and he/she doesn’t have any past records. So, the recommender system does not have enough information related to the user to recommend an item which will be of his/her interest. The dataset has been taken from the MovieLens is used in the experiment.

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.

Deep learning is advancement in machine learning with many hidden layers. It can provide better results even with less number of features. In deep learning, large scale data automated learning is possible with less feature engineering. It is of high significance for big data because of large scale of data available for training. Many applications areas such as image processing, speech recognition and social network analysis have proven to provide better results with the use of deep learning. Large scale of data is generated due to social networking sites, sensor networks and business transactions. It is very difficult for user to select any product or topic of interest, Social recommendation is active research area due to its significance in reducing information overload. It is intelligent systems which are used to provide suggestions to users for any product or topic. In this article, deep learning is applied on social recommendation technique with our proposed approach based on transitive trust....

Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. In RNN, there is limitation of not able to store high dependencies and also vanishing gradient descent issue exists. Therefore, data scientists and analysts applied LSTM to predict stock price movement. In this paper, LSTM is compared with SVR using various stock index data such as S& P 500, NYSE, NSE, BSE, NASDAQ and Dow Jones industrial Average for experiment analysis. Experiment analysis proves that LSTM provides better accuracy as compared to SVR.

Deep learning is extremely popular among data scientists and analysts due to its proven accuracy in recognizing speech, image processing, and Big Data analytics. Data is growing at exponential rate due to sensor networks and communications. It is analyzed that 90% data was generated during last 2 years and data will be increased at this pace in forthcoming years also. Deep learning has become more significant as it can be used for large scale of unlabeled data. The increase in number of hidden nodes with the sheer size of data resists learning. Deep learning with new algorithmic approaches is able to learn large-scale unlabeled data with less feature engineering. Big Data can be better analyzed by deep learning. In this paper, the focus is on new algorithms and techniques of deep learning which can provide better accuracy for large-scale complex and unstructured data.

Social big data is large scale of data due to exponential popularity of social network and social media. Researchers can use social big data and social network for their observations if they analyse those in an intelligent manner. The target of intelligent decision is to find the most credible user in social network, who has the highest influence. A very large number of users are connected in social networks, implicitly friends-of-friends or explicitly mutual friends. They are able to communicate with each other and share their likes or dislikes on different topics. If users want to analyse any topic or purchase product like movie, book, they are populated with a lot of choices. Information overload due to large number of choices available to users limits effective product selection and hence results in reduced users’ satisfaction. Recommendation models are solution for providing better suggestion to users. Product’s recommendation at Amazon, friend’s recommendation at Facebook and music recommendation at iTunes are some of the popular examples of suggestions made on the basis of user’s interests. Recommendation models ease the user by reducing search space in social network graph. The main purpose of this paper is to improve social recommendations so that better and more appropriate choices are available for users. In this paper, an efficient technique for social recommendations using hyperedge and transitive closure is proposed. Social big data is processed and analysed in the form of social graphs. User–user and user–item connections are represented in the form of matrices. We have exploited homophily so that large number of connected users have trust on each other. Our model provides better recommendation to users by leveraging increased trust. The proposed model overcomes the limitations of traditional recommender systems like sparsity, cold start. It also improves prediction accuracy. The proposed model is evaluated through different metrics like MAE, precision, recall and RMSE. Empirical analysis shows significant improvement in recommendations. We have used Mahout library for improving recommendation accuracy and also handling large volume of data. SNAP library is also used for analysis of social big graphs. The proposed recommendation model is evaluated using Epinions and FilmTrust datasets. These datasets contain user’s ratings for various products in the scale of 1–5. Through analysis it is verified that the proposed model boosts the performance significantly. We have formulated recommendation model using manipulated social graph as per our proposed technique. This manipulated graph is mentioned as influence product graph (IPG) throughout this paper. IPG increases social trust value between connected users and this effect in recommending products in an effective and efficient manner.

Objectives: To find out sentiment of people about a particular thing or objects and to classify these sentiments. Methods: The common dialect handling techniques like fuzzy logic and neural system to be used extract emotions from text present in various blogs using MATLAB. Findings: The results show that with Neural Network and Fuzzy Logic performs very well in recognizing the emotional polarity of the sentences. From result simulations it has been concluded that the proposed method worked well having accuracy of 90% and able to classify the text according to their class (Happy, Sad and Anger). Improvements: The proposed method achieves better results in terms of Accuracy, Precision, Sensitivity and Specificity.

Recommendation is very crucial technique for social networking sites and business organizations. It provides suggestions based on users’ personalized interest and provide users with movies, books and topics links that would be most suitable for them. It can improve user effectiveness and business revenue by approximately 30%, if analyzed in intelligent manner. Social recommendation systems for traditional datasets are already analyzed by researchers and practitioners in detail. Several researchers have improved recommendation accuracy and throughput by using various innovative approaches. Deep learning has been proven to provide significant improvements in image processing and object recognition. It is machine learning technique where hidden layers are used to improve outcome. In traditional recommendation techniques, sparsity and cold start are limitations which are due to less user-item interactions. This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications. Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques. When these recommendation systems are deployed on large scale of data, accuracy degrades significantly. Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) are used as evaluation parameters which are used to prove better recommendation accuracy. Epinions, MovieLens and FilmTrust datasets are also shown as most commonly used datasets for recommendation purpose.

Objectives: Its objective is to develop a system which can recognize specific human hand gesture features from images and use these features to convey information for machine such as HCI (Human Computer Interaction). Method: this paper represents that, we propose a new method that supports the hand gesture recognition system in the static form, using SIFT feature extraction with feed forward neural network using MATLAB . We use SIFT technique to extract the invariant features of gesture sign. We have developed a gesture recognition system using feed forward neural networks which could recognize a finger alphabet of different types of symbols and where each gesture specifies a word. Findings: In the process of hand gesture recognition system there are many challenges addressed as: Illuminance conditions such that a small change in the lighting conditions effects badly on extraction process like color from which may produce misclassification problem. In the various scenes when the hand region rotates in any direction then the rotation problem get arises. When there is complicated backgrounds like there are other objects in the image with hand poses, this may refer to the background problems. Scale or Size problem, this problem may occur when the hand poses have not same size in the gestured image. At last the transformation problem, like from different different images it’s difficult to represent features of various hand positions. Improvements: To remove theses hindrances we have to develop an algorithm that can effectively recognize the hand gestures. Hand gestures is one of the techniques used in security purposes and robot communication.

Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.

Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.

Big data is a collection of large scale of structured, semi-structured and unstructured data. It is generated due to Social networks, Business organizations, interaction and views of social connected users. It is used for important decision making in business and research organizations. Storage which is efficient to process this large scale of data to extract important information in less response time is the need of current competitive time. Relational databases which have ruled the storage technology for such a long time seems not suitable for mixed types of data. Data can not be represented just in the form of rows and columns in tables. NoSQL (Not only SQL) is complementary to SQL technology which can provide various formats for storage that can be easily compatible with high velocity,large volume and different variety of data. NoSQL databases are categorized in four techniques- Column oriented, Key Value based, Graph based and Document oriented databases. There are approximately 120 real solutions existing for these categories; most commonly used solutions are elaborated in Introduction section. Several research works have been carried out to analyze these NoSQL technology solutions. These studies have not mentioned the situations in which a particular data storage technique is to be chosen. In this study and analysis, we have tried our best to provide answer on technology selection based on specific requirement to the reader. In previous research, comparisons amongNoSQL data storage techniques have been described by using real examples like MongoDB, Neo4J etc. Our observation is that if users have adequate knowledge of NoSQL categories and their comparison, then it is easy for them to choose best suitable category and then real solutions can be selected from this category.