An IoT-Based Energy Conservation Smart Classroom System

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an influential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation domain.The proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we propose.Moreover,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)rates.Furthermore,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't used.The feasibility and benefits of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu branch.The results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

Attention-Based Bi-LSTM Model for Arabic Depression Classification

Depression is a common mental health issue that affects a large percentage of people all around the world.Usually,people who suffer from this mood disorder have issues such as low concentration,dementia,mood swings,and even suicide.A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods.Therefore,the analysis of social media content provides insight into individual moods,including depression.Several studies have been conducted on depression detection in English and less in Arabic.The detection of depression from Arabic social media lags behind due the complexity of Arabic language and the lack of resources and techniques available.In this study,we performed a depression analysis on Arabic social media content to understand the feelings of the users.A bidirectional long short-term memory(Bi-LSTM)with an attention mechanism is presented to learn important hidden features for depression detection successfully.The proposed deep learning model combines an attention mechanism with a Bi-LSTM to simultaneously focus on discriminative features and learn significant word weights that contribute highly to depression detection.In order to evaluate our model,we collected a Twitter dataset of approximately 6000 tweets.The data labelling was done by manually classifying tweets as depressed or not depressed.Experimental results showed that the proposed model outperformed state-of-the-art machine learning models in detecting depression.The attention-based BiLSTM model achieved 0.83%accuracy on the depression detection task.