scholarly journals Artificial Intelligence Models for Crime Prediction in Urban Spaces

2020 ◽  
Vol 8 (1) ◽  
pp. 1-13
Author(s):  
Ana Laura Lira Cortes ◽  
Carlos Fuentes Silva
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Urban Areas ◽  
Urban Context ◽  
Criminal Records ◽  
Legal Information ◽  
Legal Advice ◽  
Intelligence Services ◽  
Artificial Intelligence Models ◽  
The Government

This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

scholarly journals Relating Blindsight and AI: A Review

2021 ◽  
pp. 1-15
Author(s):  
Joshua Bensemann ◽  
Qiming Bao ◽  
Gaël Gendron ◽  
Tim Hartill ◽  
Michael Witbrock
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Models ◽  
Visual Experience ◽  
Biological Research ◽  
Future Research ◽  
Network Architectures ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Artificial Neural

Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review of research on the phenomenon of blindsight in an attempt to generate ideas for artificial intelligence models. Blindsight can be considered as a diminished form of visual experience. If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks. This paper has been structured into three parts. Section 2 is a review of blindsight research, looking specifically at the errors occurring during this condition compared to normal vision. Section 3 identifies overall patterns from Sec. 2 to generate insights for computational models of vision. Section 4 demonstrates the utility of examining biological research to inform artificial intelligence research by examining computational models of visual attention relevant to one of the insights generated in Sec. 3. The research covered in Sec. 4 shows that incorporating one of our insights into computational vision does benefit those models. Future research will be required to determine whether our other insights are as valuable.

scholarly journals Cadastral Information and Erosion Modeling for Monitoring Gullies in Mpazi Catchment Area, Kigali City

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
J.P Bizimana, ◽  
E Ndahigwa
Keyword(s):  
Urban Areas ◽  
Average Rate ◽  
Hydrological Modelling ◽  
Data Availability ◽  
Mitigation Measures ◽  
Urban Landscapes ◽  
Urban Context ◽  
Differential Gps ◽  
Ancillary Data ◽  
The Government

Due to the lack of sediment load monitoring system, erosion model calibration is challenging in Rwanda. Based on the reports of parcels boundaries corrections from Rwanda Land Management and Use Authority, there are quite consistent losses of land due to gullies development in Mpazi River watershed. This study analysed the possibility of integrating cadastral information, erosion and hydrological modelling data for identifying potential gullies development in hilly urban area of Mpazi catchment. The orthophoto of 2008 coupled with ancillary data were used to delineate the shifts of parcel boundaries from 2012 to 2016. Hydrological modelling based on DEM was also applied to investigate geo-physical characteristics and potential gullies. The differential GPS was used to locate the potential gullies that were then uploaded into GIS. Gullies intersecting with rectified parcels boundary were measured with tape meter. The gully length was measured using hydrological modelling tools and GPS coordinates captured during the field verification. The results showed that gully system expanded from 7.5 to 20 meters while neighboring parcels shift was between 3 and 12.5 meters. The highest average rate of soil loss ranged between 100 and 150 tons/ha/year. The research findings from this study are salient for policy- and decision-makers who need to review the current land and environment policies and laws so that gully erosion can be assigned appropriate mitigation measures for ecologically sustainable management of hilly urban landscapes within Kigali City. More periodic data are required to feed the model and validating this approach brings the government institutions’ responsibility. The developed methodology has the potential to quantify the gully systems in urban context with scarce hydrological, soil and geomorphological data availability and where urban planning and environmental protection are constrained by limited financial and human resources. Keywords: Cadastral Maintenance Data, Erosion Modelling, Gully, Urban Areas

An integrative approach to understand vulnerability and resilience post-disaster

2017 ◽  
Vol 26 (3) ◽  
pp. 259-275 ◽  
Author(s):  
Tony Rey ◽  
Loic Le De ◽  
Frederic Leone ◽  
David Gilbert
Keyword(s):  
Urban Communities ◽  
Urban Area ◽  
Urban Areas ◽  
High Energy ◽  
Integrative Approach ◽  
Urban Context ◽  
High Exposure ◽  
Content Type ◽  
The People ◽  
The Government

Purpose The purpose of this paper is to develop a multidimensional approach for effectively managing natural disasters; this paper has three research objectives. First, it provides an analysis on the hydro-geomorphological effects of the cyclone in the urban context. Second, it proposes an analysis for the vulnerability and resilience recovery of the populations living in urban areas. Third, it specifies the implications for sustainable recovery and longer-term disaster risk reduction. Design/methodology/approach A detailed case study of the tropical cyclone Pam was carried out to identify hydro-geomorphologic effects and damages in an urban area and specific problems associated with managing natural disaster in Vanuatu. Findings The investigations reveal that living in an urban area increases a population’s exposure to hydrological, weather and sea-related risks. Whereas advice on cyclones seems to work very well, the coastal risks and floods seem to be underestimated with a very high exposure and vulnerability to risk. Pre-existing vulnerabilities were exacerbated after cyclone Pam. However, other communities have been able to reinforce their resilience through local initiatives. The government and outside aid were very quick to react, despite problems of coordination, exchange of information, communication and long-term strategy. Practical implications The bottom-up, top-down, local and global approaches, applied on the time scales, should lead to actions that will reinforce the ability of the people of Vanuatu to adapt to high-energy events and to the effects of climate change. Originality/value This paper highlights the importance of understanding how the urban communities are vulnerable to natural hazards and of strategies for increasing their resilience.

scholarly journals Improving Access to Justice with Legal Chatbots

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 356-375
Author(s):  
Marc Queudot ◽  
Éric Charton ◽  
Marie-Jean Meurs
Keyword(s):  
Access To Justice ◽  
Legal Representation ◽  
Legal Information ◽  
Dialog Systems ◽  
Job Tasks ◽  
Legal Advice ◽  
Conversational Interface ◽  
Bank Employees ◽  
The Government ◽  
Government Of Canada

On average, one in three Canadians will be affected by a legal problem over a three-year period. Unfortunately, whether it is legal representation or legal advice, the very high cost of these services excludes disadvantaged and most vulnerable people, forcing them to represent themselves. For these people, accessing legal information is therefore critical. In this work, we attempt to tackle this problem by embedding legal data in a conversational interface. We introduce two dialog systems (chatbots) created to provide legal information. The first one, based on data from the Government of Canada, deals with immigration issues, while the second one informs bank employees about legal issues related to their job tasks. Both chatbots rely on various representations and classification algorithms, from mature techniques to novel advances in the field. The chatbot dedicated to immigration issues is shared with the research community as an open resource project.

scholarly journals A comparison of activation functions in multilayer neural network for predicting the production and consumption of electricity power

2021 ◽  
Vol 11 (1) ◽  
pp. 163
Author(s):  
Abdulwahed Salam ◽  
Abdelaaziz El Hibaoui ◽  
Abdulgabbar Saif
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Activation Function ◽  
Activation Functions ◽  
Paper Cover ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Electricity Power ◽  
Multilayer Neural Networks ◽  
Power Utilities

Predicting electricity power is an important task, which helps power utilities in improving their systems’ performance in terms of effectiveness, productivity, management and control. Several researches had introduced this task using three main models: engineering, statistical and artificial intelligence. Based on the experiments, which used artificial intelligence models, multilayer neural networks model has proven its success in predicting many evaluation datasets. However, the performance of this model depends mainly on the type of activation function. Therefore, this paper introduces an experimental study for investigating the performance of the multilayer neural networks model with respect to different activation functions and different depths of hidden layers. The experiments in this paper cover the comparison among eleven activation functions using four benchmark electricity datasets. The activation functions under examination are sigmoid, hyperbolic tangent, SoftSign, SoftPlus, ReLU, Leak ReLU, Gaussian, ELU, SELU, Swish and Adjust-Swish. Experimental results show that ReLU and Leak ReLU activation functions outperform their counterparts in all datasets.

Construction of a multi-agent attack detection system based on artificial intelligence models

2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 22-30
Author(s):  
Belej O ◽  
◽  
Spas N ◽  
Artyshchuk I ◽  
Fedastsou M ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Information System ◽  
Detection System ◽  
Attack Detection ◽  
Sources Of Information ◽  
Joint Decision ◽  
Different Types ◽  
Artificial Intelligence Models ◽  
Multi Agent

Statistics of recent years on attacking actions on information systems show both the growth of known attackers and the growth of new models and directions of attacks. In this regard, the task of collecting information about events occurring in the information system and related to the main objects of the information system, and conducting their effective analysis is relevant. The main requirements for the tools of analysis are: speed and ability to adapt to new circumstances - adaptability. Means that meet these requirements are artificial intelligence systems. In particular, there are a number of research that use neural networks as a means of analysis. There are different types of neural networks, which differ depending on the tasks to be solved and are more suitable for different input data. The proposed multi-agent attack detection system collects and analyzes the collected information about the events of the information system using two types of neural networks. A multilayer perceptron is used to analyze various logs of information system objects. The Jordan network is used to analyze directly collected information about the events of information system objects. The use of a multi-agent attack detection system can increase the security of the information system. Features of modern attacks are considered. The urgency of the task of detecting attacks is substantiated. The peculiarities of the attack process were considered. The actions of attackers of different types at different stages of the attack are analyzed. It was shown which methods of detecting attacks should be used at different stages of the attack by an attacker. A model of a multi-agent attack detection system is proposed. An interpretation of the results of the analysis of information system events by the method of detecting attacks was proposed, as well as an algorithm for joint decision-making by agents based on several sources of information about their status. A model of an attack detection system that takes into account these features is proposed. This attack detection system collects information at several levels of the information system and uses it to analyze the artificial intelligence system

Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

2020 ◽  
pp. 27-28
Author(s):  
A.B. Movsisyan ◽  
◽  
A.V. Kuroyedov ◽  
G.A. Ostapenko ◽  
S.V. Podvigin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Modern Method ◽  
Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

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