Non-linear dynamic state-space network modeling for decoding neurodegeneration

Neurodegenerative disorders represent a pervasive global health challenge,yet therapeutic options remain conspicuously limited.These disorders are inherently dynamic processes within the central nervous system,unfolding across distinct sub-stages:initial structural neuronal alterations(sub-stage 1),functional impairment(sub-stage 2),and culminating in neuronal death(sub-stage 3).

Assistive techniques and their added value for tremor classification in multiple sclerosis

Tremor occurs in about half of multiple sclerosis(MS)patients.MS tremor has a broad frequency range of 2.5-7 Hz,with a higher prevalence of postural tremor(44%)compared to intentional tremor(6%)(Alusi et al.,2001).Tremor may affect the upper and lower extremities,head,and trunk,and may even affect the vocal cords in isolated cases of palatal tremor.MS tremor is classically attributed to lesions of the brain stem,cerebellum,or cerebellar peduncles,and tremor intensity has been shown to correlate with the number of lesions or their functional connections.However,recent work has demonstrated that inflammatory damage to the cerebello-thalamic and cortico-thalamic pathways might also play an important role in causing tremor,as it co-occurs with other signs and symptoms of MS such as dysarthria,dysmetria,dysdiadochokinesia,and dystonia(Alusi et al.,2001).

Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching

In this study,a novel non-intrusive temperature rise fault-identification method for a distribution cabinet based on tensor block-matching is proposed.Two-stage data repair is used to reconstruct the temperature-field information to support the demand for temperature rise fault-identification of non-intrusive distribution cabinets.In the coarse-repair stage,this method is based on the outside temperature information of the distribution cabinet,using tensor block-matching technology to search for an appropriate tensor block in the temperature-field tensor dictionary,filling the target space area from the outside to the inside,and realizing the reconstruction of the three-dimensional temperature field inside the distribution cabinet.In the fine-repair stage,tensor super-resolution technology is used to fill the temperature field obtained from coarse repair to realize the smoothing of the temperature-field information inside the distribution cabinet.Non-intrusive temperature rise fault-identification is realized by setting clustering rules and temperature thresholds to compare the location of the heat source with the location of the distribution cabinet components.The simulation results show that the temperature-field reconstruction error is reduced by 82.42%compared with the traditional technology,and the temperature rise fault-identification accuracy is greater than 86%,verifying the feasibility and effectiveness of the temperature-field reconstruction and temperature rise fault-identification.

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past.More specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs.The cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple VirtualMachines.Each virtualmachine potentially represents a different user environment such as operating system,programming environment,and applications.However,these cloud services use a large amount of electrical energy and produce greenhouse gases.To reduce the electricity cost and greenhouse gases,energy efficient algorithms must be designed.One specific area where energy efficient algorithms are required is virtual machine consolidation.With virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement requirements.This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host.The online algorithm is analyzed using a competitive analysis approach.In addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms.Our proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.

IoMT-Cloud Task Scheduling Using AI

The internet of medical things(IoMT)empowers patients to get adaptable,and virtualized gear over the internet.Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it.Thus,a proposition is being made for a distinct scheduling technique to suitably meet these solicitations.To manage the scheduling issue,an artificial intelligence(AI)method known as a hybrid genetic algorithm(HGA)is proposed.The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches.The CloudSim is utilized to quantify its effect on various parameters like time,resource utilization,cost,and throughput.The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47ms and a reduced time of 40.16ms.Thus,the experimented outcomes show that the HGA reduces cost as well as time profoundly.

Computing the User Experience via Big Data Analysis:A Case of Uber Services

As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and anger.Subsequently,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements.The results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative influence.However,the influence of expectation confirmation on user satisfaction is not supported.Moreover,sadness,anxiety,and anger are positively related to the perceived risk of users.Compared with sadness and anxiety,anger has a more important role in increasing the perceived burden of users.Based on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services.The proposed big data approach may be utilized in other UX studies in the future.

Residential PV capacity estimation and power disaggregation using net metering measurements

As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.

Repulsive firefly algorithm-based optimal switching device placement in power distribution systems

To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.

Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

Embedding Extraction for Arabic Text Using the AraBERT Model

Nowadays,we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task.In this work,we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking,essay grading,and question answering systems.We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset.The used schemes include lexical-based similarity features,frequency-based features,and pre-trained model-based features.Also,we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers(AraBERT).We used the AraBERT model in two different variants.First,as a feature extractor in addition to the text vectorization schemes’features.We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units.Second,AraBERT is adopted as a pre-trained model,and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences.To evaluate the research results,we conducted several experiments to compare the use of the AraBERT model in its two variants.In terms of Mean Absolute Percentage Error(MAPE),the results showminor variance between AraBERT v0.2 as a feature extractor(21.7723)and the fine-tuned AraBERT v2(21.8211).On the other hand,AraBERT v0.2-Large as a feature extractor outperforms the finetuned AraBERT v2 model on the used data set in terms of the coefficient of determination(R2)values(0.014050,−0.032861),respectively.

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application services.Particularly,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere.To provide an improved service,it is important to analyze online reviews that contain user satisfaction.Several previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey data.These methods may not be appropriate for analyzing the users of mobile applications.To overcome this limitation,several researchers perform user experience analysis through online reviews and star ratings.However,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text.This variation disturbs the performance of machine learning.To alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is introduced.The current study aims to build a more accurate sentiment classifier based on machine learning with VADER.In this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

Auto-Harmonizing Chinese Folk Song with Piano Accompaniment

This paper approaches melody harmonization with piano accompaniment as a machine learning task,in a probabilistic framework. An existing sample score set of Chinese folk song with piano accompaniment is used to build a model of piano accompaniment process. The model can then be used to harmonize new melody with piano accompaniment.

A comprehensive survey on security issues in vehicle-to-grid networks

Vehicle to grid(V2G)is the most hopeful approach to transfer energy as well as information in the bidirectional way.V2G network is formed by electric vehicles which connect with smart metres for information and energy transfer in a wireless manner.Even though many security preserving schemes developed in V2G networks,they were prone to enormous number of security breaches.A countless deal of works has been done towards it,but security mechanisms in V2G networks are not effective.This survey work provides a summary about the V2G network characteristics,significance,security services and the security challenges.Moreover,this work offers a summary of some foremost security attacks on various security services such as accessibility,confidentiality,authentication,integrity and non-repudiation and the related countermeasures to make the V2G communications more protected.

Natural Image Matting with Attended Global Context

Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting and perform well in capturing spatially close information. However, these methods fail to capture global contextual information, which has been proved essential in improving matting performance. This is because a matting image may be up to several megapixels, which is too big for a learning-based network to capture global contextual information due to the limit size of a receptive field. Although uniformly downsampling the matting image can alleviate this problem, it may result in the degradation of matting performance. To solve this problem, we introduce a natural image matting with the attended global context method to extract global contextual information from the whole image, and to condense them into a suitable size for learning-based network. Specifically, we first leverage a deformable sampling layer to obtain condensed foreground and background attended images respectively. Then, we utilize a contextual attention layer to extract information related to unknown regions from condensed foreground and background images generated by a deformable sampling layer. Besides, our network predicts a background as well as the alpha matte to obtain more purified foreground, which contributes to better qualitative performance in composition. Comprehensive experiments show that our method achieves competitive performance on both Composition-1k and the alphamatting.com benchmark quantitatively and qualitatively.

Tongue coating microbiome as a potential biomarker for gastritis including precancerous cascade

The development of gastritis is associated with an increased risk of gastric cancer. Current invasive gastritis diagnostic methods are not suitable for monitoring progressIn this work based on 78 gastritis patients and 50 healthy individuals, we observed that the variation of tongue-coating microbiota was associated with the occurrenee and development of gastritis. Twenty-one microbial species were identified for differentiating tongue-coating microbiomes of gastritis and healthy individuals. Pathways such as microbial metabolism in diverse environments, biosynthesis of antibiotics and bacterial chemotaxis were up-regulated in gastritis patients. The abundance of Campylobacter concisus was found associated with the gastric precancerous cascade. Furthermore, Campylobacter concisus could be detected in tongue coating and gastric fluid in a validation cohort containing 38 gastritis patients. These observations provided biological evidence of tongue diagnosis in traditional Chinese medicine, and indicated that tongue-coating microbiome could be a potential non-invasive biomarker, which might be suitable for long-term monitoring of gastritis.

Insect classification and detection in field crops using modern machine learning techniques

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest attack.Traditional insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features accurately.Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)model.This paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex background.The 9-fold cross-validation was applied to improve the performance of the classification mod-els.The highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN model.The detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection algo-rithm.The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the insects.The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.