Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

IntroductionAdvancement in technology has boosted the availability and use of smart mobile phones. At present, the number of smart phone users is 2.71 billion across the world [1]. The major online social media (SM) platforms i.e. Facebook, Twitter and Instagram are available as mobile applications in the smart phones. Therefore, there is no need to visit cyber cafes to access them, as everything is available in the smart phones.Every piece of information shared on SM carries an emotion, sentiment or feeling. These emotions can be positive, negative and neutral. All these emotions may come from a travel trip, restaurant trip, exhibitions, movies, elections, hospital visits etc. These emotions carries some hidden information related to comfort/discomfort in related areas. Hence, there is a good scope of analyzing this information to detect the patterns of the emotions. This analysis can help us to understand the emotions of the people in respective domain and the reasons behind it.Air travel is one of the most convenient mode for long distance travel at both national and international level [2]. There are many airline service providers (ASPs) around the world. The competitive world motivates the airlines company to attract the customers. AbstractCustomer's experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer's experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer's experience. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The developed network is a variant of EfficientNet with dense and drop-out layers added. Similarly, the authors combined data augmentation with min-max normalization to increase the contrast of tumor cells. The benefit of the dense CNN model is that it can accurately categorize a limited database of pictures. As a result, the proposed approach provides exceptional overall performance. The experimental results indicate that the proposed model was 99.97% accurate during training and 98.78% accurate during testing. With high accuracy and a favorable F1 score, the newly designed EfficientNet CNN architecture can be a useful decision-making tool in the study of brain tumor diagnostic tests.

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Glioblastoma (GBM) is a stage IV aggressive malignant brain tumor, which is generally found in the cerebral hemispheres of the brain [1]. The treatment of GBM tumor is very difficult and cure is not possible in most of the cases. The treatment can only slow down the progress of the cancer and may reduce the symptoms and discomfort. The diagnosis of GBM contains neurological exams, imaging tests i.e. MRI and biopsy. The GBM tumor may occur at any age; however, the high majority of the patients are adults. The symptoms of GBM may include worse headache, nausea, vomiting and seizures (Fig. 1). Medical science is equipped with advanced devices and technology. MRI machines are able to capture high contrast images of the brain and other parts of the body. These MRI scans are very useful to diagnose and detect tumors and other defected cells. However, sufficient knowledge and experience is desirable in order to read and understand these MRI scans. Sometimes, unavailability of such trained people may delay the diagnosis process. Therefore, in order to automate the process, a classification model can be developed using machine learning methods. Artificial neural

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