In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental healt...
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In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental health hazards in Barbados for the period 2014 - 2018. Then, the binary classification was used to categorize each tweet as relevant or irrelevant. Next, the multiclass classification was then used to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. Results indicate that (combination of TF-IDF, psychometric, linguistic, sentiment and Twitter-specific features) using a Random Forest algorithm is the best feature for detecting and predicting binary classification with (87% F1 score). For multi-class classification, TF-IDF using Decision Tree algorithm was the best with (74% F1 score).
Image segmentation is a fundamental step for image processing of medical images. One of the most important tasks in this step is border reconstruction, which consists of constructing a border curve separating the orga...
Image segmentation is a fundamental step for image processing of medical images. One of the most important tasks in this step is border reconstruction, which consists of constructing a border curve separating the organ or tissue of interest from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitting procedures from a collection of data points assumed to lie on the boundary of the object under analysis. However, standard mathematical optimization techniques do not provide satisfactory solutions to this problem. Some recent papers have applied evolutionary computation techniques to tackle this issue. Such works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bézier curves by applying the firefly algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on medical images of melanomas show that this method performs well and can be successfully applied to this problem.
Developing electrochemical sensors that are both highly sensitive and environmentally sustainable is a pressing need in modern healthcare and environmental analysis. In this study, a cobalt sulfide/reduced graphene ox...
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Developing electrochemical sensors that are both highly sensitive and environmentally sustainable is a pressing need in modern healthcare and environmental analysis. In this study, a cobalt sulfide/reduced graphene oxide (CoS/rGO) nanocomposite was synthesized via a straightforward, low-cost method that integrates thiourea and cobalt acetate to form CoS, with rGO derived from recycled plastic waste. The structural and electrochemical properties of the composites were systematically investigated using cyclic voltammetry (CV) and linear sweep voltammetry (LSV). The addition of rGO, varied from 0 to 50 wt%, notably enhanced the electrical conductivity and surface activity of the sensing interface. Among the tested formulations, the composite containing 40 wt% rGO exhibited the highest performance, achieving a sensitivity of 12.4 μA mM −1 cm −2 and a detection limit of 0.2 μM, which is approximately 7.5 times lower than that of pristine CoS. Kinetic analysis confirmed that the sensing mechanism follows a pseudo-second-order model, indicative of a chemisorption-driven interaction between paracetamol molecules and the sensor surface. The sensor displayed excellent operational stability over 100 consecutive cycles and high repeatability with a relative standard deviation below 2.5 %. This work demonstrates a novel, green strategy for sensor fabrication that effectively combines electronic functionality with environmental sustainability, making the CoS/rGO nanocomposite a viable platform for next-generation sensing technologies.
2020 will be the year, in which 5G is going to be put into operation, therefore, it is fundamental that all enabling technologies are analyzed in a technical and economic way. The business sector is not very favorable...
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2020 will be the year, in which 5G is going to be put into operation, therefore, it is fundamental that all enabling technologies are analyzed in a technical and economic way. The business sector is not very favorable in investing into novel technologies. In this paper, authors propose a techno-economic model for the Multiple Input Multiple Output (MIMO) technology and compare it to the previously developed model for the Distributed Antenna Systems (DAS). Firstly, the experimentation models are analyzed. Secondly, the mathematical models are presented and the specific parameters are opted. Several experiments help comparing and contrasting the two different technologies. There are not many works concerning the techno-economic perspectives of MIMO comparing it with the DAS, this research contributes in this direction.
Group activities are an activity carried out together at the same time and place. In the office/university environment, they play a role in energy consumption. For this reason, the smart building needs group activity ...
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Group activities are an activity carried out together at the same time and place. In the office/university environment, they play a role in energy consumption. For this reason, the smart building needs group activity recognition (GAR) systems for electrical device control. Several studies on the GAR have been carried out in the outdoor environment, such as exercising, walking, or running. However, indoor group activities that share of energy consumption are found still rare, such as in meetings, seminars, and classroom activities. This study proposes a GAR method in the buildings using multi-image based on Camera through the sitting position of people and formed from the face identified from the image and visualized into the mapping. The simulation was carried out based on references from the scenario of the meeting, seminar, and class activity in the S307-room, Department of Electrical engineering and Information technology, Universitas Gadjah Mada. The Neural Network algorithm was used to the GAR. The performance was evaluated in various measures such as precision, accuracy, and recall. The result of the GAR accuracy of the learning phase was 93.33%. The results of the GAR accuracy of testing phase were 63% and the error of GAR were 37%, respectively.
Although the metal-oxide-semiconductor field-effect transistor (MOSFET) has been the dominant device for modern very-large scale integration (VLSI) circuits for more than six decades, with the dawning of a post-Moore ...
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ISBN:
(数字)9781728119274
ISBN:
(纸本)9781728119281
Although the metal-oxide-semiconductor field-effect transistor (MOSFET) has been the dominant device for modern very-large scale integration (VLSI) circuits for more than six decades, with the dawning of a post-Moore era, researchers are trying to find replacements. A foundation of modern digital computing is the encoding of digital values through a binary radix representation. However, as we enter into the post-Moore era, the challenges of increasing power density, signal noise, and device unreliability raise the question of whether this basic way of encoding data is still the best choice, particularly with novel electronic devices. Prior work has shown that binary radix encoding has some disadvantages. We argue that it is crucial to rethink the necessity of using this representation in the post-Moore era. In this paper, we review some recent development on computation-driven data encoding. We begin with stochastic encoding, a representation proposed a long time ago, discussing both its advantages and disadvantages. Then, we review several recent breakthroughs with variations of stochastic encoding that mitigate many of its disadvantages. Finally, we conclude the paper by extrapolating future directions for effective computation-driven data encoding.
This paper presents an electret-based energy harvester for leadless pacemakers by harvesting energy from the ventricular blood pressure using a PDMS/parylene electret on the surface of the cylindrical device. The prop...
ISBN:
(数字)9781728156385
ISBN:
(纸本)9781728156392
This paper presents an electret-based energy harvester for leadless pacemakers by harvesting energy from the ventricular blood pressure using a PDMS/parylene electret on the surface of the cylindrical device. The proposed device has the advantage that the space within the inner cylindrical electrode can be used to accommodate the pacer electronics so that the harvester and the pacemaker can be integrated into a single device. The device design, fabrication and charging techniques of the tubular PDMS/parylene electret are presented. Preliminary tests of a prototype harvester showed 0.25 μW output to a matched 80 GΩ load for ±63 mmHg pressure variation at 1 Hz.
The proposed IceCube-Gen2 seeks to inst.ument approximately 500 square kilometers of Antarctic ice near the geographic South Pole with radio antennas, in the hopes of observing the highest-energy neutrinos (E>1 EeV...
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A preliminary investigation is carried out by applying Z-transform technique on different types of images with sole intention to record the z-score of any binary image. Bandwidth of the images are computed, and compar...
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A preliminary investigation is carried out by applying Z-transform technique on different types of images with sole intention to record the z-score of any binary image. Bandwidth of the images are computed, and compared with that obtained from Fourier technique. Length of z-score matrix is graphically represented. Result is also analyzed for smooth, shiny and rough images. This preliminary investigation will help in sending multimedia content, precisely images through network and in signal processing which helps for the segments of cancerous portion.
Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first ...
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Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.
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