Amitkumar Baburao Ranit1 and Sangita R. Gudadhe2, 1Assistant Professor, Department of Civil Engineering, Prof Ram Meghe College of Engineering & Management, Badnera, Amravati, Maharashtra, India, 2Assistant Professor, Department of Computer Science & Engineering, Sipna College of Engineering & Technology, Amravati, Maharashtra, India
In occurrence of flood event and its damages increases in number which harming human lives and lessening economy growth, damage to property and destruction of the environment. Flood losses reduce the assets of households, communities and societies through the destruction of standing crops, dwellings, infrastructure, machinery and buildings, apart from the tragic loss of life. Early warning is a key element of Disaster Risk Reduction. The basic benefit of early flood warning system is an increased lead time for warnings at locations subject to flood risk. The degree and scale of flood hazards nowadays increases massively with the changing climate for this problem we forecasting the flood by using machine learning algorithm (ML) methods which is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly program For creating the machine learning algorithm for prediction model the historical records of flood events in addition to the real-time cumulative data of a number of rain gauges or other sensing devices for various return periods are often used. The sources of dataset are traditionally rainfall, and water level, measured either by ground rain gauges or relatively new remote sensing technologies of satellites. Prediction of flood is done by using ARIMA model and Raspberry pi model. These two models help us to predict weather flood event will occurs or not. For utilization of this model we prepare a code in python language. These models take real time data as input and provide the predicted outflow as output. The use of ARIMA model and Raspberry pi techniques for the purpose of improving the quality of data set which has highly contributed in improving the accuracy of forecast and also which dramatically increase the generalization ability of models to give signal with respective time and decrease the uncertainty of prediction. Developing early warning systems for the countries can contribute to the mitigation of flood risks and life-saving through effective utilization of these new models.
Hydrological Data, Metrological Data, ANN, ARIMA, Machine Learning, Artificial Intelligence, Raspberry Pi Model.
Husaelddin Balla, Sarah Jane Delaney and Marisa Llorens Salvador, School Of Computer Science, Technological University Dublin, Dublin, Ireland
Recently, the majority of sentiment analysis researchers focus on aspect-based sentiment analysis because it delivers in-depth analysis with more accurate results compared with traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM model uses an interactive attention-based mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, left context, and right context, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets. The results demonstrate the effectiveness of using this interactive modeling technique which enhanced the model performance.
Natural Language Processing, Sentiment Analysis, Arabic SA, Deep Learning, Opinion Mining.
Philipp Bolte1, Ulf Witkowski1 and Rolf Morgenstern2, 1Department of Electronics and Circuit Technology, South Westphalia University of Applied Sciences, Soest, Germany, 2Department of Agriculture, South Westphalia University of Applied Sciences, Soest, Germany
In agriculture it becomes more and more important to have detailed data, e.g. about weather and soil quality, not only in large scale classic crop farming applications but also for urban agriculture. This paper proposes a modular wireless sensor node that can be used in a centralized data acquisition scenario. A centralized approach, in this case multiple sensor nodes and a single gateway or a set of gateways, can be easily installed even without local infrastructure as mains supply. The sensor node integrates a LoRaWAN radio module that allows long-range wireless data transmission and low-power battery operation for several months at reasonable module costs. The developed wireless sensor node is an open system with focus on easy adaption to new sensors and applications. The proposed system is evaluated in terms of transmission range, battery runtime and sensor data accuracy.
Wireless Sensor Node, LoRa Communication, Real-Time Environmental Monitoring, Urban Agriculture.
Humera Batool1*, Weiyu Li2, Asif Nawaz3, Waheed Yousuf Ramay4 and Lixin Tian5, 1School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, China, 2School of Mathematical Sciences, Suzhou University of Science and Technology Suzhou, China, 3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 4Department of Computer Science, COMSATS university Islamabad Sahiwal Campus, Pakistan, 5Center for Energy Development and Environmental Protection, Jiangsu University, Zhenjiang, China, 5Research Centre of Energy-interdependent Behavior and Strategy, Nanjing Normal University, Nanjing, China
In past decades, application of machine learning is seen scarce in predicting housing prices as compared to internet, economy, industry and other fields. House price prediction and forecasting is arduous task. To elaborate this forecasting, house price prediction model is constructed using the machine learning technique named Deep Neural Network (DNN) using platform Keras and programming language python and decision tree. Mean square error (MSE) and Root Mean Squared Error (RMSE) were used for validation of model performance. In this study, we used house prices and their attributes data from 2012 to onwards in Nanjing city, provincial capital of Jiangsu province. As case study two districts Jianye and Xuanwu on Line 2 of Nanjing metro were selected having houses proximities of 500 meters, 500-800 meters and 800-1200 meters respectively. Our results depicts that various house attributes and proximity to metro station has high positive impact on house prices. Results showed that DNN is highly efficient predictor of RMSE, achieving 0.184 and 0.377 for Xuanwu and Jianye districts respectively. Application of Deep Neural Network minimized the difference between predicted and actual price by Mean square error. Application of decision tree also facilitated in housing prices precisely. Our proposed model could facilitate and enhance the use of DNN in house price prediction problems.In future we can improve house price prediction by using machine learning algorithms like random forest and CNN or RNN.
House price prediction, Deep Neural Network (DNN), Decision tree, Machine learning.
Oluwatobi Oyinlola1∗, Kayalvizhi Jayavel2 and Didacienne Mukanyiligira1, 1African Center Of Excellence in Internet of Things (ACEIoT), University of Rwanda, Kigali, Rwanda, 2Department of Information Technology, SRM Institute of Science and Technology, India
Digital bracelets or wrist watches, which include functionalities like calculators and unit converters, were commercially available for decades. Due to the technological advancements,they are equipped with data transferring facilities and becoming multifunctional. This paper presents design of a smart bracelet, which can identify the pairs of people during handshakes and exchange their contact details with each other. An advanced system was implemented to detect pairs of handshakes when happening concurrently as multiple pairs of people may do hand shaking at the same time when the crowd size is large in a gathering. A peak detection algorithm and a top-k algorithm were used to identify the matching hand- shakes by processing data. Communication links were also established using the Bluetooth Low Energy (BLE)technology to exchange contact in formation of people who are handshaking with each other. Finally, a smart bracelet with multiple functionalities was manufactured and its performance was evaluated.
Accelerometer, Bluetooth, Gesture recognition, System-on-chip.
Reena Malik and Sonal Trivedi, Chitkara Business School, Chitkara University, Punjab, India
Retail sector is transforming rapidly propelling by rising household income, technology advancements, e-commerce, and increased expectations. New innovative technologies are being used by the retailers in order to provide seamless and unique shopping experience to the customer. Internet of things is one of technology creating competitive advantage in the world of retailing and now smart retailing is in trend to cater enhanced customer expectations. This study focuses on the concept and applications of IoT especially in the field of retailing by using secondary data. The study will also focus on the brands that are using IoT and getting benefitted in terms of increased market share, customer satisfaction, retention and loyalty.
Automatic retail, Future retail, Internet of things, Digital, digital retailing, customer satisfaction, retail transformation, smart retailing.
David Noever, Samantha E. Miller Noever, PeopleTec, Inc., 4901 Corporate Drive. NW, Huntsville, AL, USA
A malicious firmware update may prove devastating to a host of embedded devices that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating systems. This work converts the binary headers into 1024-pixel thumbnail images to train a deep neural network and distinguish benign and malicious variants. One outcome of this image conversion enables contact with the vast machine learning literature applied to digit recognition (MNIST). Another result indicates that greater than 90% accurate classifications prove possible using image-based convolutional neural networks when combined with transfer learning methods.
Neural Networks, Internet of Things, Image Classification, Firmware, MNIST Benchmark.
Asaju Christine and Hima Vadapalli, Department of Computer Science, University of the Witwatersrand, Johannesburg, South Africa
A key problem identified in the online classes is that it lacks direct, timely, effective communication and feedback to teachers compared with the traditional face-to-face classes. Studies have shown that facial emotion expression plays an important role in communication, especially to understand the learning affects of students. This work considers a deep learning approach to improve on the current set up in the online learning domain through the use of facial emotion expression recognition and testimation of a learner’s learning affects. The study leverages facial emotion recognition using Resnet50 pre-trained CNN networks for feature extraction and a Long Short-Term Memory Network (LSTM) for classification of the emotions using extended DISFA data-set, the work achieved an accuracy of 95% on validation data set compared with the state- of- the art approach. It is expected that this will provide feedback to the teachers and cause an improvement upon the online platform.
Online learning, Deep learning, facial emotion recognition, Learning Affects.
Ali Asghar Anvary Rostamy, Professor of Finance, Tarbiat Modares University, Tehran, Iran
This study measures and compare the capabilities of a statistical model of ARIMA and three intelligent models namely artificial neural network, fuzzy neural network, and genetic algorithm in predicting stock returns of companies listed in the Tehran Stock Exchange during 2013 to 2019. Three main hypotheses considered in this research imply that the forecasting capability of the proposed models is significantly different, the intelligent models outperform the traditional statistical ARIMA model, and that the technical analysis makes fewer errors than fundamental analysis in predicting stock returns. The diversified sample of this research consists of 18 manufacturing companies from different industries. Excel, SPSS, and MATLAB software were used to analyse the data. The results indicate that the artificial neural network, fuzzy neural network, and genetic algorithm have fewer errors than ARIMA method. In other words, all of the proposed intelligent models significantly outperform the unintelligent statistical method of ARIMA.
Stock Returns, Forecasting, Intelligent Models, Iranian Stock Market.
Naveen Kumar, Mathematics Division of University Institute of Sciences (UIS), Chandigarh University, Gharuan, Mohali-140413, Punjab, India
The epidemic of coronavirus took place on 31 December 2019 when China notified the World Health Organization of a series of cases of unclear origin of pneumonia in Wuhan City, Hubei Province. The novel coronavirus disease (COVID-19) triggered clusters of lethal pneumonia with a clinical appearance extremely close to that of severe acute respiratory syndrome referred to as SARS. COVID-19 is a viral disorder that is transferable to other healthful individuals from an infected adult. COVID-19 allows certain individuals to develop minor illnesses, but in certain instances may prove harmful. Therefore, treating the possibility of infection seriously is very necessary. COVID-19 has quickly flourished as a global health epidemic that effects substantial number of a people across worldwide as illustrated by the World Health Organisation (WHO). Consequently, to stop this outbreak in an environment when no vaccinations are accessible to citizens for their wellbeing, the awareness about the sensitivity of virus is an efficient means to mitigate the spread of coronavirus. The structural views and evaluations of the Novel coronavirus were explored in the early parts of this article. The numerous methods of transmission and social opportunities have also been illuminated to minimize COVID-19. The results of social distancing were identified as preventive steps to remove COVID-19 in the last portion of this article.
Corona virus, COVID-19, SARS-CoV-1, SARS-CoV-2, Social distancing.
Asmaa Hakami, Raneem Alqarni and Mahila Almutairi, Department of Computer Science, King abdulaziz university, Jeddah, Saudi Arabia
Generating poems is not an easy task for humans, so if the computer is able to create a poem, this indicates the development of computer creativity. Many models have been used to create poems, but few of them are interested in the Arabic language, and between the lines of the entire poem, the coherence in meaning and themes is still unclear. In this paper, the character-based LSTM, Markov-LSTM, and pre-trained GPT-2 models were used to create Arabic praise poems and compare their results in terms of the accuracy of the meaning and the accuracy of the model. The results of both the Markov-LSTM and pre-trained GPT-2 were similar in terms of the meaning and outcome of the BLEU method. However, there is a weakness in terms of meaning in the character-based LSTM model because it creates new words.
Arabic Poems, Markov, GPT-2, Deep Neural Networks, & Natural Language Processing.
Shaolin Hu1,2,*, Wenqiang Jiang2, Jiahui Liang2, Jian Li3, 1Guangdong University of Petrochemical Technology, Maoming 525000, China, 2Xi’an University of Technology, Xi’an 710048, China, 3Norinco Group Test and Measuring Academy, Huayin 714200, China
If the radar is used to track a highly dynamic target, its tracking measurement data may inevitably include outliers. In order to avoid the adverse effects of outliers on target positioning, it is necessary to check the rationality of the radar measurement data. This paper establishes a set of new methods to use the measurement data of a single optical theodolite to verify the rationality of radar measurement data. Using optical theodolite measurement data to verify radar measurement data is a common and commonly used method, but usually two or more optical theodolites are required to achieve this goal. The difficulty of using a single optical theodolite measurement data to verify the radar measurement data is that the optical theodolite can only provide two kinds of data (the azimuth and elevation), but the radar simultaneously measures three data (the distance, azimuth and elevation), and more importantly, the reference datum of the theodolite and radar are different. This paper skillfully overcomes the above-mentioned problems and realizes the data rationality test in three different actual cases: in case 1, whether the radar data is normal is unknown; in case 2, the distance of radar measurement data is known to be normal, but whether the angle measurement is normal is unknown; in case 3, the distance of radar measurement data is known to be normal, but whether the range measurement is normal is unknown. Simulation results show that the correct rate is more than 95% if the optical theodolite measurement data is high-precision, and that more than 90% of the accuracy of outlier recognition can still be achieved even if the theodolite measurement data contains small measurement errors.
Rationality Test, Outliers ,Theodolite, Radar.
Btissame MAHI and Youssef FAKIR, Laboratory of Information Processing and Decision Support, Department of Computer Science, Faculty of Science and Technology, Sultan Moulay Slimane University. PO Box. 523, Béni Mellal, Morocco
Cluster algorithms can be classified as hierarchical based clustering, density based clustering, grid based clustering and model based clustering. Clustering algorithms has been studied and applied in many different areas, such as analysis of institutions academic performance. In this article, we will cover partition-based clustering algorithms for Land conflicts. The k-means approach is compared to other partition clustering algorithms, such as Partitioning Around Medoids (PAM) and Fuzzy CMeans clustering (FCM). The results of the experiments indicate that the number of clusters and the number of data can influence the performance of algorithms.
Clustering, partition, Fuzzy C-Means clustering (FCM), K-Means, Partitioning Around Medoids (PAM).
Rachid Sabre, Biogéosciences (UMR CNRS/uB 6282), University of Burgundy, 26, Bd Docteur Petitjean, Dijon, France
This work focuses on the symmetric alpha stable process with continuous time frequently used in modeling the signal with indefinitely growing variance when the spectral measure is mixed: sum of a continuous meseare and discrete measure. The objective of this paper is to estimatethe spectral density of the continuous part from discrete observations of the signal. For that, we propose a method based on the smoothing of the observations via Jackson polynomial kernel using toi spectral windows and taking into account the width of the interval where the spectral density is non-zero and sampling at periodic instant. This technique allows avoiding the aliasing phenomenonencountered when the estimation is made from the discrete observations of a process with continuous time.
Spectral density, stable processes, periodogram, smoothing estimate, aliasing.
Cem Ata Baykara1, Ilgin Safak2 and Kübra Kalkan3, 1Department of Computer Science, Ozyegin University, Istanbul, Turkey, 2Fibabanka R&D Center, Istanbul, Turkey, 3Department of Computer Science, Ozyegin University, Istanbul, Turkey
The rapid growth of the Internet of Things (IoT) has changed how people perform digital transactions in their everyday lives. Due to the increasing number of diverse and connected devices used in both public and private networks, security and privacy have gained the utmost importance. However, security is often achieved at the expense of efficiency and computational resources, where not all IoT devices are equipped with sufficient computational power. This paper proposes a new lightweight handshake protocol implemented on top of the Constrained Application Protocol (CoAP) that can be used in device discovery, autonomously and securely managing (adding or removing) devices of any computational complexity and ensuring the IoT network security. A Physical Unclonable Function (PUF) is utilized for the session key generation in the proposed handshake protocol. The CoAP server performs real-time device discovery using the proposed handshake protocol and autonomous anomaly detection using machine learning algorithms to ensure the security of the IoT network. IoT devices displaying anomalous behaviour are autonomously blacklisted by the CoAP server and not allowed to join the network or removed from the IoT network. Simulation results show that among the machine learning algorithms, the stacking classifier performs with the highest overall anomaly detection accuracy of 99.98%.
IoT Networks, Network Security, Handshake Protocols, Anomaly Detection, Machine Learning.