This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification o...
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This paper presents an efficient prediction model for a good learning environment using Random Forest(RF)*** consists of a series of modules;data preprocessing,data normalization,data split andfinally classification or prediction by the RF *** preprocessed data is normalized using minmax normalization often used before *** the input data or variables are measured at different scales,it is necessary to normalize them to contribute equally to the ***,the RF classifier is employed for course selection which is an ensemble learning method and k-fold cross-validation(k=10)is used to validate the *** proposed Prediction Model for Course Selection(PMCS)system is considered a multi-class problem that predicts the course for a particular learner with three complexity levels,namely low,medium and *** is operated under two modes;locally and *** former considers the gender of the learner and the later does not consider the gender of the *** database comprises the learner opinions from 75 males and 75 females per category(low,medium and high).Thus the system uses a total of 450 samples to evaluate the performance of the PMCS *** show that the system’s performance,while using locally i.e.,gender-wise has slightly higher performance than the global *** RF classifier with 75 decision trees in the global system provides an average accuracy of 97.6%,whereas in the local system it is 97%(male)and 97.6%(female).The overall performance of the RF classifier with 75 trees is better than 25,50 and 100 decision trees in both local and global systems.
Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake video...
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Money laundering is a serious threat to global financial systems, causing instability and inflation, and especially hurting middle-class savings. This paper suggests a new way to tackle these problems by using blockch...
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The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily fun...
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The Telecare Medical Information System (TMIS) faces challenges in securely exchanging sensitive health information between TMIS nodes. A Mutual Authenticated Key Agreement (MAKA) scheme is used to eliminate security ...
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This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodol...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of v...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.
We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also...
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We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least max{Ω(L), Ω(pLµ )} when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Copyright is held by author/owner(s).
Object detection is a significant method in computer vision, mainly for detecting objects belonging to different classes in an image. Its applications are widely spread to video surveillance, human tracking, autonomou...
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