In the area of datascience, machine learning-based classification techniques are the first choice for an accurate analysis of a huge amount of data. The first requirement while developing such classification techniqu...
Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as cr...
Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on “normal years”. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period.
Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstra...
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The Perspective-n-Point problem aims to estimate the relative pose between a calibrated monocular camera and a known 3D model, by aligning pairs of 2D captured image points to their corresponding 3D points in the mode...
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The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for compa...
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An (ε,δ)-DP mechanism is a mapping defined as follows. The domain of the mechanism is a finite set of objects, (also called the data points) such that a symmetric neighborhood relation over the data points is define...
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ISBN:
(数字)9798350387094
ISBN:
(纸本)9798350387100
An (ε,δ)-DP mechanism is a mapping defined as follows. The domain of the mechanism is a finite set of objects, (also called the data points) such that a symmetric neighborhood relation over the data points is defined. The range of the mechanism at each data point is a distribution over another set. Further more, neighboring data points must be mapped to two distributions that are not far away. The parametric notion of distance of two distribution in terms of the parameters (ε,δ) in the context of privacy theory, is first introduced by Dwork and her *** this paper, we study the following problem. Given a finite set $\mathcal{D}$ of data points, the neighboring relation, the parameters ε,δ, and a partial mechanism that is defined over a subset ${\mathcal{D}^\prime } \subseteq \mathcal{D}$, is there an extension of the mechanism defined over the entire set $\mathcal{D}$ that is identical to the partial mechanism on D
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and also, is (ε,δ)-differential private. We show that there exists an algorithm to answer this question and it runs in time that is polynomial in the input variables. Our result generalizes a result of Medard et al. about optimum mechanism extension with respect to preferential query ordering.
It is common in everyday spoken communication that we look at the turning head of a talker to listen to his/her voice. Humans see the talker to listen better, so do machines. However, previous studies on audio-visual ...
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Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, thes...
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ISBN:
(数字)9798350396157
ISBN:
(纸本)9798350396164
Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities.
With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address...
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In our daily lives, sentiment analysis is essential. People usually make decisions after seeking advice from others. We used to rely on our judgments before the internet on the advice of our friends and relatives. But...
In our daily lives, sentiment analysis is essential. People usually make decisions after seeking advice from others. We used to rely on our judgments before the internet on the advice of our friends and relatives. But now, we have high-performance source sentiment analysis, which collects and analyses opinions from a variety of channels, including social media. To correctly represent emotion in a phrase, we must concentrate on idioms, sarcasm, negations, and context terms. A range of deep learning and machine learning techniques can be used to overcome the aforementioned issues. The aforementioned parameter will be used by the algorithm to identify the polarity of a statement. This study proposal covers the problems with sentiment analysis as well as its numerous levels. The review talks about the challenges and particularities of sentiment analysis. These have to do with issues like textual information, peculiarities of language, emotional vocabulary relevant to particular domains, and security and confidentiality of data. The report also emphasizes how sentiment analysis might promote public healthcare initiatives, boost pharmaceutical methods for making choices, as well as improve patient care.
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