Internet of Things (IoT) security and reliability rely on the capacity to identify distributed denial-of-service (DDoS) assaults in IoT networks. This research presents a comprehensive study on DDoS attack detection using the NSL-KDD dataset. The dataset contains a diverse set of network traffic data. This paper proposes two approaches, one utilizing Principal Component Analysis (PCA) and another without PCA, to compare their performance. Robust scaling and encoding techniques are applied as preprocessing steps. The experiment outcomes demonstrate a noteworthy improvement in the accuracy of DDoS attack detection in IoT devices by integrating PCA and Robust Scaler. Notably, the Random Forest and KNN classifiers demonstrate exceptional performance with an accuracy of 99.87 % and 99.14 %, respectively, while Naïve Bayes shows a lower accuracy of 87.14 %. The findings from this experiment contribute valuable insights into enhancing the security of IoT devices against DDoS attacks. The proposed approach showcases the importance of appropriate preprocessing techniques in achieving robust intrusion detection systems for IoT environments.

Emotion and feelings are recently becoming popular concepts in the everyday life. It not only affects human health but also plays an essential role in the decision-making processes. For this reason, emotion classification is one of the important aspects to deal with the problems like mental disorders, suicidal activities and judgmental process. Electroencephalogram (EEG) signal is one of the physiological signals which can be collected from the human brain activity while a person performing various mental and physical task. In this paper, the DEAP dataset has been implemented with the deep learning model for the classification process. In the process of developing models, for the extraction of the important features from the unprocessed EEG signals, Fast Fourier Transformation is used. Three ensemble deep learning models are tested and compared to get the best accuracy result for emotion classification. Furthermore, by the best model we can classify four emotional regions in the valance-arousal plane: HVHA, HVLA, LVHA and LVLA can be classified. The experimental results show that among all the three deep learning models, 1D-CNN-GRU achieved the highest training accuracy of 96.54

The proliferation of mobile computing and cloud services is driving a revolutionary change in our information society. We are moving into the Ubiquitous Computing age in which a user utilizes, at the same time, several electronic platforms through which he can access all the required information whenever and wherever needed. The mobile devices provides the easiest solution for ubiquitous access through wireless network. Mobile users can use their cellular phone to check e-mail, browse internet; travelers with portable computers can surf the internet from airports, railway stations etc. The mobile capabilities can be integrated with cloud computing services to give more secure and advanced services to the subscribers. The emerging domain of Mobile-Cloudextends the Mobile Computing paradigm to the sharing of cloud resources in distributed computing environment. A Mobile-cloud is the result of the integration of mobile application with the cloud. In this paper we propose a Mobile Cloud Computing architecture to integrate mobile application with various cloud services. Our Paper aims at using cloud computing techniques for storage and processing of data on mobile devices, thereby reducing their limitations.

The proliferation of mobile computing and cloud services is driving a revolutionary change in today's information society. We are moving into the Ubiquitous computing age in which a user utilizes, at the same time, several electronic platforms through which one can access all the required information whenever and wherever needed. Mobile users can use their cellular phone to check e-mail, browse internet; travelers with portable computers can surf the internet from airports, railway stations etc. The mobile capabilities can be integrated with cloud computing services to give more secure and advanced services to the subscribers. At the same time privacy is an important issue in the collaborative ubiquitous computing since privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. The main challenge arises as to how multiple parties collaboratively conduct information exchange without breaching data privacy. This paper seeks to investigate solutions for secure Mobile cloud architecture by using a privacy preserving K-Medoids clustering which is one of data mining tasks.

Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feedforward networks are trained using back propagation learning algorithm.

AbstractProjection displays comprise a multibillion‐dollar market, served by four distinct technologies. This market is driven by sales of high‐volume consumer products and high‐value business systems, with different technologies covering various application markets. The consumer market is dominated by rear‐CRT technology, especially in the large‐screen TV segment, but rear LCD, DLPT, and LCOS projection TVs are now emerging as competitors to rear CRT projectors. Front CRT projectors are mostly confined to the high‐end home theater market. While front LCD and the rapidly growing front DLP projectors dominate the business market, front LCOS projectors are obtaining a larger share of the market. Recent developments in projection technology are resulting in improvements in picture quality while enabling a reduction in prices, thus opening up new opportunities in the emerging consumer/home theater market. This paper discusses the trends involving projection technology in the emerging home theater market.

Ultraportable projectors (LCD, MEMS/DLP and LCOS) have become essential tools for the business presentation market. Manufacturers are breaking barriers every year in weight, size, and brightness, and are offering projectors with increased features and lower prices. The next generation of projectors can work more as information technology devices with wireless connectivity and networking abilities. The current status and future trends of business projectors will be presented in this paper.