A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13]

Convolutional Neural Network를 이용한 한국어 영화평 감성 분석

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

Framework for Improving Critical Infrastructure Cybersecurity News From Down Under

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018GL080768

Attribution of Global Soil Moisture Drying to Human Activities: A Quantitative Viewpoint

Health information systems R G Rowe, MFCM, and R D Brittain, MD.... 951 Beh et's disease H Yazici, MD; P J Gow, FRACP.......... 952 Preventing Rh haemolytic disease L A D Tovey, FRCPATH ................ 952 Syringe-transmitted hepatitis S M Laird, FRCPGLAS; H Gordon, MFCM.. 953 Misdiagnosis of amoebiasis D S Ridley, FRCPATH, and D C Warhurst, PHD .................................. 953 Blood cultures for diagnosis of endocarditis E Joan Stokes, FRCPATH, and A Hollman, FRCP .................................. 953 Bell's palsy and herpes simplex B E Juel-Jensen, FRCP; Constance A C Ross, FRCPATH .............................. 953 Antibiotic resistance in Streptococcus pneumoniae and Haemophilus influenzae A J Howard, MB, and J D Williams, MD.... 954 Antibiotics in endocarditis B C Stratford, FRACP; Celia M Oakley, FRCP 954 Falls and femoral fractures T P Eddy, FFCM ...................... 955 Asthma in children J K Sarsfield, MD; N C H Stott, MRCPED .... 955 Abuse of pentazocine St Kubicki, MD; T A Betts, MRCPSYCH .... 955 Swaddling and congenital dislocation of the hip M P M Richards, PHD ..... ..... 956 Health of King Henry VIII R J Hetherington, MB ................... 956 Aminoglycosides in patients with impaired renal function K R Woodcock, MRCP; P Noone, MRCPATH 956 The Year of the Child: an objective for rich nations Isabel Smith, MRCP, and D C Morley, FRCP 957 Central venous catheter embolism J L Peters, FRCS; D Verel, FRCP.......... 957 Misuse of hypnosis H B Gibson, PHD, DIPPSYCH............ 957 Patient package inserts J D Collinge, MRCGP .................... 958 Side effects of antibiotics in mountain climbers A Pines, FRCPED ........................ 958 Successful defibrillation in general practice A D Shaw, MRCGP, and J L Baird, MRCP .... 958 When and why are babies weaned? Penny A Stanway, MB .................. 958 Contamination of sterile fluids G Prout, BSC .......................... 959 Abuse of asthma cigarettes RGH Bethel,MB ....................... 959 Closed shop in the USA J H Shepherd, FRCS .................... 959 Inhibition of fibrinolysis and abruptio placentae C Merskey, FRCP, and others; G J Kleiner, MD, and Wilma M Greston, MT.......... 959 Cardiac arrhythmias due to chloral hydrate poisoning Heather M Wiseman, MSC, and G Hampel, M .................................. 960 Rubella vaccination and pregnancy S R Preblud, MD, and others ............ 960 Iodine and acetone-containing plastic spray dressings P R W Lanham, FFARCS .................. 961 Neonatal chlamydia conjunctivitis J W T Benson, MRCP, and D C Davidson, MRcP ................................ 961

Health information systems.

Detection of rice pest and diseases, and proper management and control of pest infested rice fields may result to a higher rice crop production. According to the International Rice Research Institute, farmers lose an average of 37% of their rice crops due to pest and diseases, yearly. Using modern technologies, like smart phones, farmers can be aided in detecting and identifying the type of pests and diseases found in their rice fields. This study proposed an application that will help farmers in detecting rice insect pests and diseases using Convolutional Neural Network(CNN) and image processing. It looked into the different pests that attack rice fields; information on how they can be controlled and managed was considered; farmers' knowledge in different rice pests and diseases, and how they control these pests was regarded in this study; the study also looked into the reporting mechanism of farmers to government agencies. Using CNN and image processing, the application that detects rice pests and diseases was developed. The searching and comparison of captured images to a stack of rice pest images was implemented using a model based on CNN. Collected images were pre-processed and were used in training the model. The model was able to achieve a final training accuracy of 90.9 percent. Cross-entropy was low, which implies that the trained model can perform prediction or can classify images with low percentage of error. Through the developed application, farmers were provided with information and procedures on how to control and manage rice pest infestation. Future researchers may look into multiple pest comparison to a stack of images for faster retrieval of information.

Rice Pest and Disease Detection Using Convolutional Neural Network

One of the emergent concerns of human life is about health and wellness. Undeniably, health and nutrition are one of the valuable aspects of life. Thus, technological innovations to help enhance and even promote health awareness is essential. With the advent of mobile computing, it is much easier to be aware of health information because of its mobility and availability. Much mobile application is being developed to serve as a tool for health monitoring and nutritional guide. Mobile applications have the ability to support health needs like detecting heart rate, classifying food, and many more. Taking advantage of technology, utilization of it hereby addresses certain issue and problems of human life, especially in health. In this study, the researcher's attempts to design and develop an Android-based food recognition application that could be used as a health awareness tool for non-health conscious individual. The application lets the user take the photo of the food and show its nutritional contents. Implementing Mifflin-St Jeor method in determining daily calorie consumption, users shall be aware of their required calorie intake. Moreover, the researchers' have studied its effect on people's health awareness on food nutrition by the randomly selected respondents. Finally, this paper presents an analysis of the impact of the food recognition app to change people's concept of food nutrition.

NutriTrack: Android-based food recognition app for nutrition awareness

Denial of Service (DOS) and (DDOS) Distributed Denial of Service attacks have become a major security threat to university campus network security since most of the students and teachers prepare online services such as enrolment, grading system, library etc. Therefore, the issue of network security has become a priority to university campus network management. Using online services in university network can be easily compromised. However, traditional security mechanisms approach such as Defense-In-Depth (DID) Model is outdated in today’s complex network and DID Model has been used as a primary cybersecurity defense model in the university campus network today. However, university administration should realize that Defense-In-Depth (DID) are playing an increasingly limited role in DOS/DDoS protection and this paper brings this fact to light. This paper presents that the Defense-In-Depth (DID) is not capable of defending complex and volatile DOS/DDOS attacks effectively. The test results were presented in this study in order to support our claim. The researchers established a Defense-In-Depth (DID) Network model at the Central Luzon State University and penetrated the Network System using DOS/DDOS attack to simulate the real network scenario. This paper also presents the new approach Defense-through-Deception network security model that improves the traditional passive protection by applying deception techniques to them that give insights into the limitations posed by the Defense-In-Depth (DID) Model. Furthermore, this model is designed to prevent an attacker who has already entered the network from doing damage.

https://www.beei.org/index.php/EEI/article/download/1349/878

Defense-through-Deception Network Security Model: Securing University Campus Network from DOS/DDOS Attack

Educational Data Mining can help stakeholders give appropriate decisions to improve educational experiences. New knowledge or models are realized when data mining techniques are applied on educational data. This study focuses on building classification model by utilizing data mining techniques for predicting the likelihood of a student to pass the Licensure Examination for Teachers (LET). Several well-known data mining algorithms such as Neural Network, Support Vector Machine, C4.5 Decision Tree, Naive Bayes, and Logistic Regression are used to build the models. The performance of these models in terms of accuracy to predict the student's performance in the Licensure Examination for Teachers, F1 Measure and Area under the Curve (AUC) value were compared to determine which among these classification algorithms performs best. Results show that C4.5 turns to be the most suitable algorithm for the model. It has an accuracy of 73.10%, F1 measure of 62.53% and Area under the Curve value of 0.730. The identified model could be able to identify students who will likely fail the Licensure Examination for Teachers. These students should be given higher priority during their mock board review and be able to pass the board examination. Aside from helping these students, it also helps the institution get higher percentage of passers in the Licensure Examination for Teachers and can be of help during accreditation.

Predicting Student's Board Examination Performance using Classification Algorithms

These days, because of increment in relocation from a provincial territory to urban ranges, the population in urban areas is obviously expanding quickly together with the requirement for comfortable living. With increase in population, urban areas have expanded, water becomes one of the major problems in a city particularly water distribution, interfered with water supply, water protection, water utilization and furthermore the water quality. To overcome water supply related problems proper monitoring and controlling system must be implemented. The developed system consist of different IoT devices like water pressure sensor, ultrasonic sensor, solid state relay switch, motorized electric water valve, Raspberry PI, GSM module and Arduino UNO micro-controller. This paper focused on the monitoring and controlling of water distribution using IOT based model. It aims to design and develop a low cost reliable and efficient technique to improve water distribution in the community. A prototype was developed to simulate the operation of a water distribution. Also, a web application was created as a front-end system for monitoring the status of the different pumping stations as well as controlling. Also, fuzzy logic algorithm was integrated into the developed prototype system to be more scientific in making decision. As a result, the experiment was successful and passed all the conditions set for monitoring and controlling water distribution using IoT based model.

https://iopscience.iop.org/article/10.1088/1757-899X/482/1/012045/pdf

Thelma D. Palaoag

Papers

Rice Pest and Disease Detection Using Convolutional Neural Network

NutriTrack: Android-based food recognition app for nutrition awareness

Defense-through-Deception Network Security Model: Securing University Campus Network from DOS/DDOS Attack

Predicting Student's Board Examination Performance using Classification Algorithms

IoT based model for monitoring and controlling water distribution