the paper presents the Electronic Voting Machine (EVM) device to facilitate digital elections withthe use of Raspberry Pi as its brain. It is developed to reduce paper wastage and prevent fraud and ghost voting in co...
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ISBN:
(纸本)9798350395600;9798350395594
the paper presents the Electronic Voting Machine (EVM) device to facilitate digital elections withthe use of Raspberry Pi as its brain. It is developed to reduce paper wastage and prevent fraud and ghost voting in contrast to the Paper Ballot System. the EVM is designed to authenticate the voter through fingerprint or face authentication. the face authentication method is incorporated with an anti-spoofing technique. Local Binary pattern Histograms (LBPH) and Haar-Cascade Classifier models were used to detect and recognize faces and eye-blink counters for anti-spoofing. Both authentications were combined and incorporated into the voting graphic user interface (GUI), programmed through Python and its libraries to create the EVM, wherein the voter must authenticate through fingerprint or face recognition if they failed the prior method. the accuracy tests resulted in an overall 80% and 95.56% accuracy for fingerprint and face authentication, respectively. the antispoofing technique resulted in an overall accuracy of 100%. the mock elections of 20 voters conducted to test the functionality of the EVM resulted in an 80% fingerprint authentication success rate, while 100% of the remaining 20% passed the face authentication. those who failed the fingerprint authentication proceeded to face authentication, resulting in an overall 100% success rate in the authentication of all voters. the database results matched the receipt of the voters, demonstrating the device's overall efficiency of 100%. In conclusion, the paper proposes an efficient and secure EVM that ensures fair and transparent digital elections.
the study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance wi...
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the patternrecognition and computer-assisted translation teaching under the school-enterprise cooperation mode is centered on computer-assisted translation technology, and the implementation of networked interactive ...
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Normally, a patternrecognition has been studied in the field of digital signal processing with digital codes. Even though it has very accurate results, it needs a lot of power consumption, very long operating time, a...
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Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their suscepti...
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Fault diagnosis includes both detection of the type of fault that has occurred as well as locating the fault on a vast stretch of transmission network. there exists wide scope of application of Artificial Intelligence...
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ISBN:
(纸本)9781665473804
Fault diagnosis includes both detection of the type of fault that has occurred as well as locating the fault on a vast stretch of transmission network. there exists wide scope of application of Artificial Intelligence (AI) and Machine Learning (ML) based patternrecognition techniques in fault detection. the method proposed in this paper attempts to correctly identify the location of fault, type of fault and the faulty phase of transmission line in the form of an encoded 13bit binary number. this number is then decoded and appropriate remedial measures are taken to isolate the fault. In this paper, Decision Tree (DT) and Random Forest based classification techniques have been proposed for fault diagnosis in a radial transmission line. Python platform has been used to simulate the proposed fault classifiers and implemented on a developed SIMULINK model of a 100 km long radial transmission line. the proposed method delivered fault recognition accuracy in the range of 95 - 100% under different fault scenarios.
the need of creating improved that are better equipped to manage 'big data'has significantly grown due to the growing volume of data in electromyographic signal study. As a result, increasingly sophisticated c...
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the proceedings contain 97 papers. the topics discussed include: sequential frequency estimation using auxiliary particle filter;gamification of donation app: what affect donators’ decision?;a self-augmentation trans...
ISBN:
(纸本)9781665489126
the proceedings contain 97 papers. the topics discussed include: sequential frequency estimation using auxiliary particle filter;gamification of donation app: what affect donators’ decision?;a self-augmentation transfer learning network for image deraining;towards employee-driven idea mining: concept, benefits, and challenges;the classification of edible-nest swiftlets using deep learning;disambiguation of web search terms based on clustering using page rank and distance between words;adaptive noise cancellation using a fully connected network: a lesson learned;research on self-driving based on dynamic recognition of traffic signs;data clustering using particle swarm optimization for pairwise microarray bioinformatics data;system design of a toxic waste management system empirical study for Chiang Rai province;shape recognition using unconstrained pill images based on deep convolution network;beauty face: an android application for cosmetic consumers to try on and receive product recommendation;and in-memory synchronization platform for electronic toll collection via computer network of the expressway in thailand.
In the disciplines of enterprise, electronic commerce, bioinformatics, and other related subjects, sequential form mining is one of the most broadly utilized approaches. Traditional techniques are unable to accurately...
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