Sentiment Analysis in Poems in Misurata Sub-dialect

Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.

Blockwise Analysis of Health Indicators of Gadchiroli District using Composite Index: An application of GKG Algorithm

Gadchiroli district is a part of Vidarbha, Traibal district away from 200 KM from sub capital of Maharashtra, Nagpur, Gadchiroli is well known tribal district of vidarbha. Total forest cover of the district is around 78% of total geographic area. District has 12 blocks with 6 revenue division. It is a notified tribal district having 8.61%–81.50% tribal population in different blocks and block-wise urbanization varies from 0.00% to 37.10%. Objective: Objective of present research paper is to device tool to rank the blocks of the district according to community health status exist in blocks and accordingly assess community health status at block level in Gadchiroli district.

Home Automation Using Packet Tracer and ESP8266

The technology has been growing from day to day in human life. The basic need of human to lead his/her life comfortably is a home. A home with updated latest technology which means a smart home. In such a scenario, there is a need to have an endeavour to have everything at the push of a button away, and more importantly, automated. Home Automation is such an endeavour, in which, all the electrical appliances present at home are connected to each other, having interactions with sensors placed at strategic positions in a closed loop manner to perform meagre tasks automatically, leaving less burden on the humans. With this project we are promoting the fact that Home Automation can greatly contribute to energy conservation too. This paper gives the basic idea use cisco packet tracer to implement smart home. Cisco implemented (IoT) functionalities in the latest version of the platform, and now it is possible to add all the smart devices, sensors, actuators, and devices, which simulate microcontrollers like Arduino or Raspberry Pi to the network. All IoT devices can be run on generic programs or modified by Java, Python or Blocky programming them. This makes Cisco Packet Tracer a perfect method to construct functional simulations for IoT.

An Economic Model of Machine Translation

With the advent of free, online translation services such as Google Translate, many people are now able to obtain information relatively effortlessly from a wide variety of foreign language sources. The translations from these services are often worse than those provided by professional, human translators, however, and the tradeoff between these two alternatives is not always clear. When should a professional be used, and when is machine translation sufficient? In this study, we discuss factors involved in the decision and illustrate their use with a predictive model.

Digital Business Model Innovation: Empirical insights into the drivers and value of Artificial Intelligence

The business activities of traditional industrial companies have commonly focused on products and product-related services. Digital pioneers have evolved their offerings into product-service systems that are networked, intelligent, personalized, and adaptable. The speed at which business models must change continues to be underestimated by many market participants, especially when order books are well filled and the pressure to change appears to be low. Industrial and service companies need to adapt to the changes induced by new market players better today than tomorrow to secure future business success and remain competitive in the digital age. The aim of this article is to intensify the debate on digital business model innovation in industry and the service sector and to enrich it with practical examples of the successful implementation of artificial intelligence in products and services.

A Tool Development Framework to Support Design Thinking for Software Engineering

This research aims to create a framework to guide the development of design thinking support tools - that is, tools that enable people to express themselves creatively and develop as creative thinkers. The main goal is to develop advanced software and social networking sites that empower users to not only be productive, but also have new technologies. Potential users of these interfaces include software with other engineers, various scientists, product and image designers, builders, teachers, students and many more. Improved communication methods can enable effective psychological search, improved interaction between groups, and faster recovery processes. These advanced combinations should also provide strong support for hypothesis formation, rapid testing of alternatives, improved visual perception, and better distribution of results.

An Empirical Model For Validity And Verification Of Ai Behavior: Overcoming Ai Hazards In Neural Networks

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. This paper discusses hazards in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems with a particular focus on ANN. The paper provides a review of previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems with a focus on neural networks. Finally, the paper considers the high-level question of how to think most productively about the safety of forward-looking applications of AI.

The Effect of ‘Datafication’ on Knowledge Diffusion: A Topological Framework

This paper begins by distinguishing between data infrastructure, data entry and data points as three distinct, but interrelated situations. Data practices are understood in the general sense of the word here, i.e., such as actions, actions, and consequences, of introducing data-generating technologies for knowledge codification. This paper will investigate both the generics and specificities of data practices to explore the disentanglement of the liveness of data practices, i.e. how such practices are happening with regard to knowledge codification. Within this regard, this study seeks to account for the ‘fluid and heterogeneous ontology’ of such practices. In other words, the framework conceptualizes data processing as correlational, and aims to provide a technique to explore they disentanglement of these relationships.

Code Generation from Simulink Models with Task and Data Parallelism

In this paper, we propose a method to automatically generate parallelized code from Simulink models, while exploiting both task and data parallelism. Building on previous research, we propose a model-based parallelizer (MBP) that exploits task parallelism and assigns tasks to CPU cores using a hierarchical clustering method. We also propose amethod in which data-parallel SYCL code is generated from Simulink models; computations with data parallelism are expressed in the form of S-Function Builder blocks and are executed in a heterogeneous computing environment. Most parts of the procedure can be automated with scripts, and the two methods can be applied together. In the evaluation, the data-parallel programs generated using our proposed method achieved a maximum speedup of approximately 547 times, compared to sequential programs, without observable differences in the computed results. In addition, the programs generated while exploiting both task and data parallelism were confirmed to have achieved better performance than those exploiting either one of the two.

Developing a Conversational Agent to Explore Machine Learning Concepts

This study aims to introduce a discussion platform and curriculum designed to help people understand how machines learn. Research shows how to train an agent through dialogue and understand how information is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy based on existing research and integrates a wide range of different subject documents into a set of key AI literacy skills to develop a user-centered AI. This functionality and structural considerations are organized into a conceptual framework based on the literature. Contributions to this paper can be used to initiate discussion and guide future research on AI learning within the computer science community.