There is a critical need for research in proactive and predictive management of the resilience of transportation systems implementing new technologies. Cooperative Intelligent Transportation System (C-ITS) uses wireless technology to allow vehicles and infrastructure to talk to each other in real-time. This makes it easier for people to work together on the road and makes it possible to make safer and more efficient traffic flows. Significant progress may be made in the transportation industry as a result of the incorporation of self-powered sensors into C-ITS providing resilience in transportation operation. One advantageous feature is that these sensors, which generate their power, could be deployed in a variety of C-ITS implementation scenarios. To assist decision-makers in making the most informed choice possible concerning investments and implementations, a type-2 neutrosophic number (T2NN) based VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is used to perform advantage prioritization. To accomplish this goal, a case study is carried out to determine which of the three alternatives is the most suitable based on a set of twelve criteria that is divided into four aspects. According to the findings, the applicability and short-term benefits are the most crucial factors in determining which option is the most advantageous for the use of self-powered sensors in C-ITS. This is because both of these factors have an immediate impact on the system.
The increasingly widespread use of IoT devices in healthcare systems has heightened the need for sustainable and efficient cybersecurity measures. In this paper, we introduce the W-RLG Model, a novel deep learning approach that combines Whale Optimization with Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for attack detection in healthcare IoT systems. Leveraging the strengths of these algorithms, the W-RLG Model identifies potential cyber threats with remarkable accuracy, protecting the integrity and privacy of sensitive health data. This model’s precision, recall, and F1-score are unparalleled, being significantly better than those achieved using traditional machine learning methods, and its sustainable design addresses the growing concerns regarding computational resource efficiency, making it a pioneering solution for shielding digital health ecosystems from evolving cyber threats.
One hundred forty one basmati rice genotypes collected from different geographic regions of North Western Himalayas were characterized using 40 traits linked microsatellite markers. Number of alleles detected by the abovementioned primers were 112 with a maximum and minimum frequency of 5 and 2 alleles, respectively. The maximum and minimum polymorphic information content values were found to be 0.63 and 0.17 for the primers RM206 and RM213, respectively. The genetic similarity coefficient for the most number of pairs ranged between of 0.2-0.9 with the average value of 0.60 for all possible combinations, indicating moderate genetic diversity among the chosen genotypes. Phylogenetic cluster analysis of the SSR data based on distance divided all genotypes into four groups (I, II, III and IV), whereas model based clustering method divided these genotypes into five groups (A, B, C, D and E). However, the result from both the analysis are in well agreement with each other for clustering on the basis of place of collection and geographic region, except the local basmati genotypes which clustered into three subpopulations in structure analysis comparison to two clusters in distance based clustering. The diverse genotypes and polymorphic trait linked microsatellites markers in the present study will be used for the identification of quantitative trait loci/genes for different economically important traits to be utilized in molecular breeding programme of rice in the future.
Abstract Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.
Given their functionality, all smartphone brands are the same. Their similarities notwithstanding, they supply the same product at different prices in the same market. Strangely enough, the consumers do comply and willingly pay such price premiums. This study examines the mediation effect of price premium and brand preference on the causal impact of brand equity on sustainable purchase intention. The novelty of this study is in transforming the initial measures in a 5-point Likert scale into continuous values through a fuzzification and defuzzification process. Brand equity comprises three factors: brand awareness, perceived quality, and prestige value. Standardized questionnaire collected data in two countries (Taiwan and Indonesia) on two brands of smartphones (iPhone and HTC). Overall, 404 questionnaires were distributed in Taiwan, and 434 questionnaires were distributed in Indonesia. The data were analyzed by applying a structural equation model after conducting an exploratory and confirmatory factors analysis. In order to improve the estimations’ accuracy, the initial measures in a 5-point Likert scale were transformed into continuous values through a fuzzification and defuzzification process. The former consisted of assigning triangular fuzzy numbers, and the latter entailed assigning a center of gravity to each triangular fuzzy number and then extracting a random number from a normal distribution function based on the center of gravity. According to the results, price premium and brand preference exhibited significant mediation effects, with price premium having stronger effects than brand preference. Furthermore, the mediation effect was strongest for perceived quality and weakest for perceived prestige value.
Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management. In the first layer of stacked models, Bi-directional LSTM is used to extract the features, and then in the second layer, the LSTM model will make the predictions. The model captures complex temporal dependencies by processing multivariate time series data in both forward and backward directions using bi-directional LSTM layers. Trained with the Mean Squared Error loss and Adam optimizer, the model demonstrates improved forecasting accuracy, offering significant potential for proactive drought management.
Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning framework tailored for DR diagnosis. The proposed framework utilizes the EfficientNetB0 architecture to classify diabetic retinopathy severity levels from retinal images. By harnessing advanced techniques in computer vision and machine learning, the proposed model aims to deliver precise and dependable DR diagnoses. Continuous testing and experimentation shows to the efficiency of the architecture, showcasing promising outcomes that could help in the transformation of both diagnosing and treatment of DR. This framework takes help from the EfficientNet Machine Learning algorithms and employing advanced CNN layering techniques. The dataset utilized in this study is titled 'Diagnosis of Diabetic Retinopathy' and is sourced from Kaggle. It consists of 35,108 retinal images, classified into five categories: No Diabetic Retinopathy (DR), Mild DR, Moderate DR, Severe DR, and Proliferative DR. Through rigorous testing, the framework yields impressive results, boasting an average accuracy of 86.53% and a loss rate of 0.5663. A comparison with alternative approaches underscores the effectiveness of EfficientNet in handling classification tasks for diabetic retinopathy, particularly highlighting its high accuracy and generalizability across DR severity levels. These findings highlight the framework's potential to significantly advance the field of DR diagnosis, given more advanced datasets and more training resources which leads it to be offering clinicians a powerful tool for early intervention and improved patient outcomes.
Due to the rapid increase in Internet of Things (IoT) devices in entrepreneurial environments, innovative cybersecurity advancements are needed to defend against escalating cyber threats. The present paper proposes an approach involving univariate feature selection leading to Sustainable IoT security. This method aims at increasing the efficiency and accuracy of the deep Convolutional Neural Network (CNN) model concerning botnet attack detection and mitigation. The approach to obtaining Sustainable IoT Security goes beyond the focus on technical aspects by proving that increased cybersecurity in IoT environments also fosters entrepreneurship in terms of stimulation, knowledge increase, and innovation. This approach is a major step towards providing entrepreneurs with the necessary tools to protect them in this digital era, which will enable and support the defense against cyber threats. A secure, innovative, and knowledgeable entrepreneurial environment is the result of Sustainable IoT security.
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