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In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network security. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. There have been a lot o...
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In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network security. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve efficient detection with a small number of false alarms. In this case, deep learning techniques are appropriate and effective algorithm to categorize both normal and attacked information. Hence, a novel a feature selection-whale optimization algorithm-deep neural network (FS-WOA-DNN) method is proposed in this research article to mitigate DDoS attack in effective manner. Initially, pre-processing step is carried out for the input dataset where min-max normalization technique is applied to replace all the input in a specified range. Later on, that normalized information is fed into the proposed FS-WOA to select the optimal set of features for ease the classification process. Those selected features are subjected to deep neural network classifier to categorize normal and attacked data. Further to enhance the security of proposed model, the normal data are secure with the help of homomorphic encryption and are securely stored in the cloud. The proposed algorithm will be simulated using the MATLAB tool and tested experimentally that shows 95.35% accuracy in detecting DDoS attack.
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Organic-inorganic hybrid perovskite single crystals have gained attention as a promising optoelectronic material due to their exceptional structural perfection and low trap densities. In this study, MAPbBr3 (Methylammonium lead br...
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Organic-inorganic hybrid perovskite single crystals have gained attention as a promising optoelectronic material due to their exceptional structural perfection and low trap densities. In this study, MAPbBr3 (Methylammonium lead bromide) single crystals were grown via inverse temperature crystallization under ambient circumstances. The powder XRD confirmed the cubic crystal structure with the Pm 3 m space group. The FTIR peak at 1424 cm-1 indicated bending vibrations in CH3NH3+ cations attached to PbBr3 structure. UV-visible spectroscopy revealed an absorption band edge at 574 nm, and photoluminescence analysis showed a green emission band at 375 nm excitation. The third-order non-linear optical (NLO) response was confirmed for the grown crystal using the Zscan technique. Though substantial progress has been made in perovskite research, a gap remains in exploring their mechanical properties which were investigated using Meyer's law, HK law, and elastic-plastic deformation law along (010) and (100) crystallographic planes. Subsequently, these two crystallographic planes were used as photo-active material for photodetectors, demonstrating a remarkable sensitivity to the whole solar spectrum.
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The transduction of solar energy to electrical energy requires Solar Photovoltaic (SPV) systems, which must be operated at Maximum Power Point (MPP) to extract maximum possible power. Being dependent on environmental factors such ...
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The transduction of solar energy to electrical energy requires Solar Photovoltaic (SPV) systems, which must be operated at Maximum Power Point (MPP) to extract maximum possible power. Being dependent on environmental factors such as irradiation and temperature, the MPP and, therefore, the SPV system's performance is nonlinear. A Maximum Power Point Tracking (MPPT) controller is usually employed, which guides the SPV systems to work at MPP. For this task, in this paper, an Adaptive Robust Fuzzy Proportional-Integral (ARFPI) controller for MPPT of an SPV system is proposed. The proposed ARFPI controller parameters have been tuned using Particle Swarm Optimization by minimizing an equal-weighted combination of Integral of the Time-Weighted Absolute Error (ITAE) and Integral of the Absolute Error (IAE). In this combination, ITAE penalized long-term errors offering a faster response, while IAE penalized aggregate errors offering lower ripples. The MPPT performance of the proposed controller has been assessed using undershoot and steady-state ripples for several varying irradiance and temperature profiles with real-world data. Further, to assess its relative performance, it has also been benchmarked against traditional MPPT techniques, i.e., perturb and observe, incremental conductance, and PID controller. The presented investigations revealed clear superiority (reduced ripples and under-shoot) of ARFPI controller, and therefore it is concluded to be a potential MPPT controller for the SPV system.
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The inconvenience and risk associated with the regular use of invasive blood glucose measurements has led to tremendous research in this area. This paper proposes the design of a non-invasive blood glucose estimation system using ...
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The inconvenience and risk associated with the regular use of invasive blood glucose measurements has led to tremendous research in this area. This paper proposes the design of a non-invasive blood glucose estimation system using novel Mel frequency cepstral coefficients features of wristband photoplethysmogram signal and physiological parameters. A dataset from 217 participants of a hospital in Cuenca Ecuador is used to validate the proposed model. The support vector regression (SVR) and extreme gradient boost regression (XGBR) techniques are used to estimate blood glucose levels (BGL). The XGBR technique achieves the least value for the standard error of prediction (SEP), 9.78 mg/dL. Further, 5 features are selected from the feature set based on the feature importance in XGBR. The XGBR model with the reduced feature set results in further reduction of SEP value (5.53 mg/dL) with a correlation coefficient of 0.99. Standard Clarke error grid analysis and Bland-Altman analysis shows that the predicted glucose values are in the clinically acceptable region. The results of the proposed model demonstrate the potential of wearable BGL monitoring technology.
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In this paper, a methodology based on nullors and passive elements to create equivalent circuits for existing building blocks has been presented. This methodology has been used for generating the equivalent nullor circuit for Curr...
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In this paper, a methodology based on nullors and passive elements to create equivalent circuits for existing building blocks has been presented. This methodology has been used for generating the equivalent nullor circuit for Current Differencing Buffered Amplifier (CDBA) and its implementation through AD844 ICs of Current Feedback Operational Amplifier (CFOA) has been presented. The proposed circuit is further modified by replacing the equivalent nullor sections with smaller blocks. The implemented CDBA (proposed CDBA-I) has been simulated and compared with existing topologies of CDBA to represent its proper functioning using LTSPICE. The proposed CDBA configuration offers a symmetric structure for its 2 differential inputs and offers higher bandwidth. Moreover, the configuration has been modified further to achieve low noise output terminal by the use of another CFOA (proposed CDBA-II). Both of these proposed configurations have been simulated and verified experimentally.
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In this paper, a gain enhanced transconductance amplifier having high bandwidth and improved phase margin has been proposed. Miller compensation technique, using a series connected capacitor and resistor branch has been utilized t...
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In this paper, a gain enhanced transconductance amplifier having high bandwidth and improved phase margin has been proposed. Miller compensation technique, using a series connected capacitor and resistor branch has been utilized to improve the phase margin of adaptively-biased cross-coupled high gain transconductance amplifier (proposed OTA-I). Further, gate compensation resistor is utilized to achieve bandwidth extension (proposed OTA-II). This proposed structure offers improvement in phase margin and bandwidth without increasing the power consumption or supply voltage requirement, while maintaining DC gain and linear range of the gain enhanced transconductance amplifier. The simulation results have been carried out in EldoSpice using TSMC based level 53, 0.18 mu m CMOS technology with +/- 0.6V supply voltage. These results show that for peaking free response in proposed OTA-II, phase margin of 59.40 degrees and bandwidth of 33.31MHz can be achieved compared to values of 51.92 degrees and 16.10MHz in conventional OTA. Monte Carlo and temperature analysis demonstrate robustness of the proposed circuit against process and temperature variations.
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This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investiga...
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This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investigated on 29 popular benchmark functions which included six CEC-2005 functions, and its performance has been benchmarked against seven optimization techniques including recent ones. Further, different abilities of LCBO optimization algorithm such as exploitation, exploration and local minima avoidance were also investigated and have been reported. In addition to this, scalability is tested for several benchmark functions where dimensions have been varied till 200. Furthermore, two engineering optimization benchmark problems, namely pressure vessel design and cantilever beam design, were also optimized using LCBO and the results have been compared with recently reported other algorithms. The obtained comparative results in all the above-mentioned experimentations revealed the clear superiority of LCBO over the other considered metaheuristic optimization algorithms. Therefore, based on the presented investigations, it is concluded that LCBO is a potential optimizer for engineering problems.
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This work aims to develop a subject-independent Emotion Detection System (EDS) based on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database physiological signals are considered for the system design. A ...
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This work aims to develop a subject-independent Emotion Detection System (EDS) based on EEG signals and the 3D Valence-Arousal-Dominance (VAD) model. The DEAP database physiological signals are considered for the system design. A multi-domain feature extraction is performed using the Wavelet-based Atomic Function time-frequency domain technique; and various time and frequency domain feature extraction techniques. Further, principal component analysis reduces the data dimensionality and redundancy in the obtained feature set. The minimal feature set is analysed using machine learning classifiers, i.e., gradient boosting, decision tree, and random forest. Additionally, the hyperparameters of machine learning algorithms are tuned using Optuna to improve the performance of the proposed model. Three EDS are designed in this work; EDS1 considers the 3D VAD model for three class classifications. 2D VA model is used in EDS2 to determine 9 discrete emotions, and EDS3 detects 12 discrete emotions using the 3D VAD model. Results reveal the highest classification accuracy of about 99% with EDS1, whereas an average accuracy of 99.82% and 98.44% is obtained with EDS2 and EDS3, respectively. The results reveal that both EDS1 and EDS2 show improvement as compared to the existing literature. Also, the proposed EDS3 provides the classification of the highest number of discrete emotions, which may facilitate the design of an efficient human-computer interface system.
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We investigate a flat FLRW-model in f (R, T)-gravity, which includes the quadratic variation in scalar curvature R and the linear term of the trace of the stress-energy tensor T. In turn, we establish the model has the behaviour o...
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We investigate a flat FLRW-model in f (R, T)-gravity, which includes the quadratic variation in scalar curvature R and the linear term of the trace of the stress-energy tensor T. In turn, we establish the model has the behaviour of the late time Universe, which is accelerated expanding. Using the parametrization of scale factor a(t), we propose a model, which begins with pointtype singularity, i.e., the model starts with a point of zero volume, infinite energy density and infinite temperature. The model's behaviour is accelerated expanding at present and Lambda CDM in late times. Finally, the proposed model behaves like a quintessence dark energy model in the present time and is consistent with standard cosmology Lambda CDM in late times.(c) 2022 Elsevier Inc. All rights reserved.
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Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained ...
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Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.
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