Top PDF AI Based Crime Detection System

AI Based Crime Detection System

AI Based Crime Detection System

The prime reason for execution of this framework is to expand the security in times when the wrongdoing scale is at its pinnacle. This framework gives the direct warning to what game-plan is being prepared in the particular area. The article identification framework has a high rate of exactness relying on the preparation gave to the framework, for example, the items it can identify are the ones we train it for instance, in this AI Based Crime Detection System we focus on the weapons part or the particular instruments sort of articles that are superfluous in a spot like ATM, for example, hammer, screwdriver, blades and so on that are unrelatable to the circumstance, the framework identifies these item by means of the picture it has caught and through likelihood it precisely advises the article being used to the specialist and police and future move can be made after that.
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An Artificial Intelligence (AI) Defect Detection Technology Based on Software Behavior Decision Tree

An Artificial Intelligence (AI) Defect Detection Technology Based on Software Behavior Decision Tree

Abstract. With the increase of software system complexity, a high requirement of the reliability, stability and security of software quality is put forward. At present, artificial intelligence (AI) defect detection adoptes machine learning technology to realize code scanning and semantic analysis on software defects. The traditional machine learning technology for software defect detection is generally based on algorithms such as BP neural network model, Naïve-Bayes model, and fingerprint identification model, etc. Regarding the features of software artificial intelligence (AI) defect detection, this paper proposes a layered detection technology based on software behavior decision tree model. Furthermore, a corresponding test environment is established to make contrast test of previously tested software. The results of the experiment shows that, with the comprehensive consideration of building time cost and false alarm rate and other factors, the artificial intelligence (AI) defect detection technology based on software behavior decision tree model is superior to other technologies.
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Improved K Mean Algorithm for Detection Rate in Intrusion Detection System with AI

Improved K Mean Algorithm for Detection Rate in Intrusion Detection System with AI

three clusters and when k=2 would provide a more natural fit. Likewise, if a group of individuals were easily clustered based upon home state and you called the k-means algorithm with k=20 then the results might be too generalized to be effective. But finding the value of i that best suits of data is very difficult. Hence we moved on to hill climbing. Hill climbing is good for finding a local optimum (a good solution that lies relatively near the initial solution) but it is not guaranteed to find the best possible solution (global optimum) out of all possible solutions (search space) which can be overcome by using steepest ascent Modified Hill climbing finds globally optimal solution. The relative simplicity of the algorithm makes it a popular first choice amongst optimizing algorithms and it is widely used in artificial intelligence, in order to reach a good state from a start state. Selection of next node and starting node can be varied to give a list of related algorithms. This can often produce a better result than other algorithms when the amount of time available to perform a search is limited, such as with real-time systems. Artificial Intelligence approach based Hill climbing algorithm attempts to maximize (or minimize) a target function ƒ(x) where x is a vector of continuous and / or discrete values. In each iteration, hill climbing will adjust a single element in x and determine whether the change improves the value of ƒ(x). Then, x is said to be globally optimal Artificial Intelligence approach based Hill Climbing aided k-means Algorithm steps are shown bellow. Input: randk - random value of kΔk - A random move in cluster
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Investigation Recommendation System Using AI

Investigation Recommendation System Using AI

ABSTRACT: Our Mission is city without Crime and increasing trust about the police an online comprehensive crime reporting system to engage public, NGOs, police and government agencies to be greaterbrief, proactive and responsive to combat with crime and criminals. One of the aims of Artificial intelligence (AI) is the realization of natural dialogue between human beings and machines. In recent years, the dialogue structures, also called interactive conversational systems are the fastest developing area in AI. Many companies have used the dialogue systems technology to establish various kinds of Virtual Personal Assistants based on their applications and areas, such as Microsoft’s Cortana, Apple’s Siri, Amazon Alexa, Google Assistant, and Facebook’s M. However, in this proposal, we have used the multi-modal dialogue systems which process two or more combined user input modes, such as speech, image recognition. Smart Virtual Assistant plays the important role to launching the FIR with speech to text conversion and after analyze the complaint apply appropriate laws with authenticate by Unique Identification (UID) for serious offense. As well as we are providing online web application to register non serious offense complaint.To enhance the communication between police and public which enables to improve the time usage for solving crimes and not an awful lot time is wasted to speak with police.
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Flexible Content Based Video Surveillance System          for crime Prevention based on moving object
          detection

Flexible Content Based Video Surveillance System for crime Prevention based on moving object detection

A framework for a video database system is explained by Yan Yang et al. [6] in year 2009, with tagging structure; which provided the ability to perform automatic search and retrieval for surveillance videos simultaneously. The video source can be any image grabber device such as a web camera. The processed frame stream is stored in a database while monitoring. In this method, tagging is the most important function where tags are stored in the database after processing. Then input SQL queries are fired on the database to find matching tags, retrieving all the related frames as output. This saves the time of searching the needed information. The biggest drawback of this system in extending it to real world applications is the data size. As the size of data to be processed increased, the performance of the system decreased. Mahapatra et al. [7], proposed a method consisting of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection. The first step helps to detect all the foreground objects in an image sequence or video by background modeling. In the second step, from the silhouettes, contours of the foreground objects are extracted. In the third part; the three extracted features are aggregated into single feature vector using Fuzzy Inference System. Then in the last part; the contour is recognized, if the aggregated feature matches to a class of template database.
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Intrusion Detection System using AI and Machine Learning Algorithm

Intrusion Detection System using AI and Machine Learning Algorithm

Network-based intrusion detection systems are placed at certain points within a network in order to monitor traffic from and to devices within the network. They operate on the same concept as wiretapping. They "tap" into a network and listen to all communication that happens. The intruder could try to minimize his network activity, but the risk is lower. NIDS are also more portable than HIDS. They monitor traffic over a network and are independent of the operating system they run on. The system can analyze the traffic using multiple techniques to determine whether the data is malicious. There are two different ways to analyze the network data. Packet-based analysis uses the entire packet including the headers and payload. An intrusion detection system that uses packet-based analysis is called a packet-based network intrusion detection system. The
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Advanced Driver Assistant System with AI Control Machine

Advanced Driver Assistant System with AI Control Machine

The main aim of this paper is to develop a automobile safety and security with autonomous region based automatic car system.we proposed three distinct but closely related concepts viz. a drowsy driver detection system and a traffic detection system with external vehicle intrusion avoidance based concept.we have to incorporated driver alert system by monitoring both the driver’s eyes as well as sensing as well as the driver situation based local environment recognition based AI system is proposed.
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Automated AI Based Road Traffic Accident Alert System: YOLO Algorithm

Automated AI Based Road Traffic Accident Alert System: YOLO Algorithm

training for field-specific tuning task when insufficient training images in entered into the system, resulting in significant improvement of performance. The method is named RCNN (CNN-enabled regions) because Regional proposals are combined with CNNs. The whole object detection system can be combined in three maine modules. Firstly, it produces region proposals which are independent,categorically. These categorical regions defines the candidate detection set which is used by our detector. The second module includes a convolutional neural network, in this modules we produce an attribute vector which are of constant length, from every region proposals. The final module, includes a cluster of support vector machine of linear nature which are specific to the class used for evaluation and assortment of regions [8].
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AI Based Traffic Signal Control System

AI Based Traffic Signal Control System

[2] Traffic control at a roundabout. It discusses the differences between system setup and vehicle recognition, tracking and routing systems and all technologies based on driving vision in a car. Recently, all directional cameras and parameters set by man are used in roadside systems, but they are faced with automation problems. Technologically integrated vehicles use light detection and ranging systems, as well as radar and stereo systems, which is a continuous overview. In the recent side system literature, vehicle tracking is primarily based on a rare feature combined with the Canada-Lukas-Tomasi settlement algorithm. In addition, relocation, modeling and prior knowledge are necessary for the exact location of the site and the correct classification of participants. When used in vehicles, the first goal is to precede or close the vehicle. Traffic signs are mainly used for various adaptations of the optical data stream. When a vehicle is detected, it is usually followed by general Bayesian algorithms, especially Kalman and particle filters. The latter optimized optical catadioptics and arc optics for the entire controlled vision is used.
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AI Based Fault Detection on Leaf and Disease Prediction using K means Clustering

AI Based Fault Detection on Leaf and Disease Prediction using K means Clustering

Cucumber crop was handled from pest infection by CLASE(central lab of agricultural expert system).four image processing method are used enhancement segmentation , feature extraction and classification to investigate disorder from leaf image .They tested three different disorders such as leaf miner ,powdery and downy. Errors have been highly been reduced between system and use by the following methods. Prasad Babu and Srinivasa Rao worked in recognition of leaf [10] disease by back propagation neural network. To determine the species from the leaf, it was proved that only black propagation network is applicable. Prewitt edge detection and thinning algorithm is used to find back propagation algorithms and leaf tokens as input. Experimentation with large training sets to recognise various leaves with pest and rotten leaves due to insects or diseases can be done as an advancement method as predicted in several reports.
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A Decision Tree Algorithm Based System for Predicting Crime in the University

A Decision Tree Algorithm Based System for Predicting Crime in the University

The result of this work showed how to deal with crime prediction using decision tree techniques. Data mining had been applied in various areas of security, crime and criminal detection. In this study, encouraging results were obtained, a sample data was used for testing and training classifiers due to time constraint. It is appropriate to perform the experiments with very large training and testing datasets as well as making a number of trials to come out with more accurate classifiers. Finally, the Students Services Department of any University can react quickly and adequately to crime situations. This makes identification of criminals more efficient and more focused. Reduction in the number of pending cases and in the population of falsely accused individuals can be achieved.
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A Comparative Study on AI based Anti terror Crime System

A Comparative Study on AI based Anti terror Crime System

Findings: The proposal module collect relevant data to terror suspects with Big Data, analyze the realistically high terror-risk data, but filtered and stored, in two different phases, and generate and modify each rule based on extracted pattern of the analyzed data. Then, consistent monitoring on risky data of probable terrors is performed in concerns of the saved analysis and Crime Response Modules are triggered on the basis of the consequent results.Such a proposal module can improve the accuracy of related date, but save duration time in collection of particular data as it only collects relevant data to terror risks via Big Data Source and Filtering Module compared to the existing module. Furthermore, the operation manuals of 1st/2nd phase Data Analysis Modules systematically analyze the terror risks and probabilities, thus can minimize the risk of actual terrorisms in fields by triggering Terror Crime Response Modules rapidly by phases with an increased terror prediction accuracy. As Monitoring Device is additionally incorporated, constant supervision and feedbacks regarding certain risk data relevant to terrors are enabled - as a result, detailed analysis of high risk data is available.
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VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System

VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System

The reliability of voluntary compliance is questionable as studies have shown that 80% of the vehicles in an unmon- itored HOV lane are in violation of the law. With the ever spreading urban sprawl and an overwhelming dependency of US cities on automobiles, decongestion is one of the highest priorities. We have developed VPDS, an AI based vehicle passenger detection system to effectively enforce HOV/HOT lane movement. VPDS automates and improves identification of HOV violators and assigns fines and tolls to HOV lane users. Moreover, it is extremely fast and takes less than 2s for classifying a vehicle as a violator or a non-violator with 96% accuracy without thwarting the normal traffic flow and using minimal hardware. Over a period of 2 years during which VPDS was deployed at three different sites, it has served approximately 30 million passengers. Serving roughly 1800 vehicles in morning and 2300 vehicles in the evening rush hours at one particular site, VPDS achieved an accuracy between 94-96% irrespective of the traffic flow or time of the day. This exemplifies an AI-based system which is highly accurate, consistent, fast, responsive in real-time, robust to externalities and requires little maintenance. In future, we aim to further improve the efficiency of VPDS by exploring recent advancements in object detection methods for better ROI detection and more powerful neural architectures for bet- ter classification. We also envision to make a holistic system with vehicle type identification as a sub-module working in conjugation with VPDS to automatically generate toll/fine so that congestion and violation management happen in a seamless manner.
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AI-powered banana diseases and pest detection

AI-powered banana diseases and pest detection

Earlier investigations have validated AI-based recogni- tion of crop diseases in wheat [12], cassava [11] and on datasets of healthy and diseased plants [8, 13]. Crop dis- ease recognition based on a computerized image system through feature extraction has revealed promising results [14] but extracting features is computationally rigorous and involves expert knowledge for robust depiction. Only few restricted large, curated image datasets of crop dis- ease library exists [10]. The PlantVillage platform holds over 50,000 images of different crops and diseases [15]. However, most of these images were taken with detached leaves on a plain background, and CNN trained on these images did not achieve well when using real field images [8]. To build robust and more practical detection mod- els, plenty of healthy and diseased images taken from different infected parts of the plants, and growing under different environmental conditions are needed. These images subsequently need to be labeled and pre-screened by plant pathology experts. So far, existing crop disease detection models are mostly focusing on leaf symptoms. Unfortunately, numerous symptoms also appear in other parts of the plant and the best examples are banana pest and disease linked symptoms.
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Edge AI System for Pneumonia and Lung Cancer Detection

Edge AI System for Pneumonia and Lung Cancer Detection

Computer vision problems are addressed by traditional architectures, But those are depend upon hand-crafted features. The deep learning algorithms have presented a promising alternative to computer vision problems. The key feature of deep learning algorithms are automatic training and learning using context/problem based specific features. With this new standard, most of the computer vision problems are re-visited using deep learning standpoint. Choosing a right deep network is very critical in solving the problems. The most relevant deep network for computer vision problems is convolutional neural networks (CNNs). “AlexNet” is a base form of CNN. Of late, many variations to CNNs are proposed based on the application domain.
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A Design of Integrated Quality Management System Based on Artificial Intelligence (AI) Technology

A Design of Integrated Quality Management System Based on Artificial Intelligence (AI) Technology

Business decision models and continuous improving strategies related to contemporary quality management needs AI algorithms to enforce intelligence and adaptability. Emerging philosophies and business tactics that looks beyond total quality management and six sigma add complexity to IQMIS. AI based IQMIS will consider a large wide of technology integration, and play an important role of product innovation, design and gross root autonomy in quality management.

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A General Study of Associations rule mining in Intrusion Detection System

A General Study of Associations rule mining in Intrusion Detection System

In [24] paper, authors have integrated two technique data mining and fuzzy technique. Where fuzzy association rules have applied to design and implement an abnormal network intrusion detection system. Here author presents that when the association rules used in traditional information detection cannot effectively deal with changes in network behavior, it will better meet the actual needs of abnormal detection to introduce the concept of fuzzy association rules to strengthen the adaptability. Basically in This paper author mainly focused on the study of Denial of Service (DOS). According to the author’s experimental results, they have found that their system can correctly identify all DOS attacks on test after appropriate adjustment of system parameters. Moreover, they have also proved, in the experiment, that their system would not result in false positives under such circumstances as a large amount of instantaneous FTP normal packet flow. In addition, if source of an attacker can be determined, the system will also be able to promptly inform the firewall to alter its rules and cut off the connection. According to another research network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks.
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Cyber-physical-system-based smart water system to prevent flood hazards

Cyber-physical-system-based smart water system to prevent flood hazards

The connection level comprises the on-site sensors. Various types of sensors were in- stalled on the basis of the study ’ s requirements. The connection level is the first level of the DayuSWS that initiates all system functions. Therefore, the information recorded by this level is crucial to trigger the other levels. The sensors were set up after carefully analyzing the locations to ensure that the collected information is suitable. A scenario involving rainfall for 3 h and 150-mm accumulated rain water, with no water outflow from the park, was considered in this study. The simulated results are presented in Fig. 3. The results revealed the west side of the park to have a high risk of flooding, which is consistent with the topology of the area. Because all the vital facilities such as power and water treatment plants are located on the west side of the park, a total of six water gauges were installed on this side, as shown in Fig. 1. Figure 4 is the pictures of all on-site gauges: the gauge locations from left to right and beginning at the top cor- respond to the numbered locations in Fig. 1. Gauges 5 and 6 were installed to monitor the water levels in pond A and the Hsinchuangzhi drainage, respectively, and are cru- cial to understanding the water exchange between the park and outside area. Gauge four was installed to monitor the water level in pond B. The rain water collected on the east side is monitored by gauge three and then transferred to pond B. Moreover, gauges one and two were installed to monitor the water collected on the upper side of the park (red rectangle in Fig. 3). A rainfall gauge was installed at location 6 to monitor the park ’ s rainfall. The real-time observations of all the sensors are wirelessly transferred to the big-data platform at an interval of 10 min.
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AI Based Student Bot for Academic Information System using Machine Learning

AI Based Student Bot for Academic Information System using Machine Learning

college-related information such as timetables, notes, etc. This system also helps the user to get the updated information’s about the college activities. The query will be answered on the basis of the knowledge base. The keywords will be fetched from the natural language processing algorithms and a relevant answer will be provided to the user. In case the answer is not available in the knowledge base or the question is irrelevant then the default message will be displayed. A. AIML

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An Abnormity AI Detection Method of Breast Mammography

An Abnormity AI Detection Method of Breast Mammography

Frequency-tuned algorithm[14] is considered on the basis of its capability to generate saliency map from an input image. The method defines the saliency of each pixel in the image by comparing the local color and brightness features, and preserves the boundary of the ROI by retaining more frequency information, which is simple and efficient, and has good performance on image detection without complicated background. The steps involved in computing the saliency map are explained below:

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