Deep learning predicts availability of mobile crowdsourced services spatially and temporally.
problem Predicting the availability of mobile crowdsourced services in space and time.
method Two-stage prediction model: clustering services into regions, then forecasting availability duration using time series.
result Effectiveness validated through multiple experiments.
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
problem Predicting service impact from network failures.
method Temporal multimodal CNN for predicting time to recovery and traffic loss.
result DeepSIP reduced prediction error by approximately 50%.
TAPAS speeds up encrypted machine learning predictions.
problem Data privacy and computation time in encrypted machine learning.
method Combining binarization, sparsification, and homomorphic encryption techniques.
result Significantly reduces computation time for encrypted predictions.
Framework optimizes transit routes based on crowd movements using demand prediction and supply optimization.
problem Dynamic optimization of transit routes in areas of crowd movements.
method Combines demand prediction (Quantile Regression) and supply optimization (Linear Programming) to dynamically redesign routes.
result Framework often obtains optimal solutions and outperforms conventional methods.
Predict and explain service failures in supply-chain networks using data models.
problem Predict and explain service failures in supply-chain networks, particularly last-mile pickup and delivery.
method Used supervised classification with Random Forests and Association Rules on a dataset of 500,000 services.
result Classifier reaches an average sensitivity of 0.7 and specificity of 0.7 for 5 types of failure.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Boosting algorithms improve delivery time prediction in postal services.
problem Challenges in long-term travel time prediction for postal services.
method Investigated linear regression models, tree-based ensembles (random forest, bagging, boosting), and compared their performance.
result Boosting algorithms, especially light gradient boosting and catboost, outperform other methods in accuracy and runtime efficiency.
ServeNet classifies web services without manual feature engineering.
problem Manual feature engineering limits the performance of conventional machine learning methods in web service classification.
method ServeNet uses a deep neural network to automatically abstract service names and descriptions into high-level features.
result ServeNet achieves higher accuracy and robustness in web service classification compared to other machine learning methods.
Proposes a trust model for SIoT nodes using Hellinger distance and matrix factorization.
problem Trust management in SIoT to reduce risk from malicious nodes.
method Flexible bipartite graph, Hellinger distance, centrality, similarity measures, matrix factorization.
result The proposed trust prediction mechanism outperforms existing methods in accuracy and resilience.
ChOracle predicts user return times to improve churn prediction.
problem Churn prediction in online services.
method Combining Temporal Point Processes and Recurrent Neural Networks with latent variables.
result Superior performance on various real-world datasets.
AI agent predicts industry and product/service codes for companies.
problem Manual curation of company data is expensive and prone to errors.
method Hierarchical multi-class industry code classifier with multi-label product/service code classifier.
result High accuracy (92-96%) achieved with limited labeled data.
Economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy. By combining tools from network science and econometrics, a robust and stable relationship between a country's productive structure and its economic growth has been established. Here we report that not only …
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video strea…
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…
With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, the…
Cloak identifies essential features to preserve prediction privacy without provider collaboration.
problem Discovering the subset of features necessary for a prediction task.
method Gradient-based perturbation maximization method to identify essential features, followed by suppression of the rest using utility-preserving constant values.
result Cloak reduces mutual information between input and sifted representations by 85.01% with negligible utility loss.
Study explores fairness in financial deep learning through multi-scale trust quantification.
problem Ensuring fairness in financial deep learning models, especially under regulatory compliance.
method Conducts multi-scale trust quantification on a deep neural network for credit card default prediction.
result Demonstrates the feasibility and utility of multi-scale trust quantification for financial deep learning fairness.
Optimizes resource allocation for virtualized network functions based on performance profiles.
problem Mapping SLA performance requirements to dynamic virtualized infrastructure resources.
method Profile-based resource allocation using VNF performance datasets and machine learning models.
result A method to predict and recommend optimal resource allocation for network services.
Non-parametric method predicts multi-stream longitudinal data evolution.
problem Predicting the evolution of multi-stream longitudinal data for an in-service unit.
method Decomposes each stream into eigenfunctions and FPC scores, uses Gaussian process prior and empirical Bayesian updating.
result Framework outperforms state-of-the-art approaches and achieves high predictive accuracy.
This paper develops a framework for predicting patient workload across multiple VA facilities.
problem Handling demand uncertainty and predicting workload in healthcare facilities, especially chronic disease treatment.
method Developed a heuristic clustering algorithm for single task learning and a multi-task learning approach.
result Demonstrated improved accuracy in workload prediction for patients with similar conditions across multiple facilities.
This paper presents a bus travel time prediction system using deep neural networks.
problem Accurate travel time predictions for urban buses to compete with other modes of transport.
method Multi-output, multi-time-step deep neural network combining convolutional and LSTM layers.
result The proposed model significantly outperforms other methods and detects small irregular peaks quickly.
This research predicts the popularity of new video contents using a hybrid machine learning approach.
problem Predicting the popularity of new video contents before they are published.
method Hybrid machine learning approach combining XGBoosting and deep neural nets, using metadata and categorical embedding techniques.
result Achieved better performance than standalone methods, validated on a dataset from a top streaming service.
Deep-Gap predicts crowdsourcing supply-demand gaps using deep learning.
problem Balancing supply and demand in mobile crowdsourcing.
method Residual learning-based deep neural networks trained on time series data and external factors.
result Deep-Gap achieves lowest forecasting errors compared to state-of-the-art methods.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Digital Financial Services continue to expand and replace the delivery of traditional banking services to the customers through innovative technologies to meet the growing complex needs and globalization challenges. These diversified digital products help the organizations (service providers) to improve their firm perf…
This paper improves bidding price prediction for ancillary services markets, boosting revenues.
problem Volatility in renewable energy sources affects grid stability and revenue optimization.
method Machine learning models (SVR, DT, k-NN) and offset adjustment for pay-as-bid markets.
result The proposed approach increases potential revenues by 27.43% to 37.31% compared to baseline models.
DiffQue estimates relative difficulty of questions in CQA services.
problem Estimating relative difficulty of questions in community Q&A services.
method Network-aided edge directionality prediction.
result DiffQue outperforms state-of-the-art methods by significant margins.
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
Models predict fire and other emergencies in Edmonton.
problem Accurate prediction of emergency events for timely response.
method Data collection, descriptive analysis, feature selection, and negative binomial regression.
result Models perform well, with acceptable prediction errors for weekly and monthly periods.
Paper uses RNN to predict SaaS user lifetime value.
problem Predicting user lifetime value in SaaS applications.
method Recurrent Neural Network with multi-cell architecture, accounting for cohort, age-in-system, and contemporaneous information.
result Significantly improved prediction accuracy compared to existing models.
Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some …
The paper improves patient targeting in pharmaceutical sales by predicting treatment delays.
problem Improving accuracy in predicting treatment delays for patients.
method A time-sensitive targeting framework using a time series model with extracted features and look-forward periods.
result Improved accuracy in predicting treatment delays for patients.
Private equity deals predict public market returns with up to 70% accuracy.
problem Predicting public market behavior from private equity transactions.
method Logit model using detailed analysis of private equity diligence process.
result Model predicts public market returns with up to 70% accuracy in specific sectors.
NetDP predicts loan defaults using network data, addressing cold-start issues.
problem Cold-start problem in default prediction for new users.
method Combines unsupervised and supervised network representations, using parameter-server for scalability.
result Effectiveness in cold-start problem, especially for new users.
This paper compares neural networks with traditional methods for churn prediction using financial data.
problem Churn prediction with sequential data and deep neural networks.
method Assesses LSTM neural networks combined with RFM variables against logistic regression models.
result LSTM neural networks outperform traditional methods in churn prediction.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
problem Real-time market prediction requires accurate predictions before a deadline.
method Filters feasible models, dispatches workers, trades accuracy for deadline risk.
result Optimized pool achieves 0.991 timely accuracy and 0.994 raw accuracy.
Paper uses deep reinforcement learning for network slicing and traffic prediction.
problem Managing dynamic network slices while maintaining QoS in a 5G environment.
method Integrates LSTM for traffic prediction and DDRL for distributed decision-making.
result Significant improvements in network performance, reducing QoS violations.
Modeling shared mobility demand considering supply limitations.
problem Inaccurate demand predictions due to limited supply.
method Censored Gaussian Processes for demand modeling.
result Taking supply limitations into account improves demand predictions.
OTT services are replacing traditional telecom services, affecting revenue streams.
problem OTT services are reducing traditional telecom revenue streams.
method Quantitative analysis of mobile user trends and revenue changes.
result OTT services are becoming more prevalent and impacting telecom revenue.
FrugalML optimizes API selection for cost and accuracy.
problem Challenges in choosing the best ML prediction APIs.
method Proposes FrugalML, a framework that learns API strengths and weaknesses.
result Achieves up to 90% cost reduction with matching accuracy.
Paper proposes machine learning for pricing 3D printing services in marketplaces.
problem Inefficient pricing methods for 3D printing services in marketplaces.
method Data mining and machine learning methods to estimate price ranges based on supplier and customer characteristics.
result Machine learning model achieves 65% accuracy for US suppliers and 59% for Europe suppliers in classifying 3D printer listings.
Noise injection improves inference privacy in DNN models.
problem Malicious servers can infer sensitive attributes from input data.
method Adaptive Noise Injection (ANI) using a lightweight DNN on the client.
result Significant improvement in privacy (up to 48.5% degradation in sensitive-task accuracy with <1% degradation in primary accuracy).
Proposes a neural network solution for queuing dynamics.
problem Modeling service system dynamics with limited parametric assumptions.
method Combines recurrent neural networks and generative adversarial networks.
result Evaluates solution on various datasets, demonstrating effectiveness.
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
problem Adoption and integration of decentralized finance (DeFi) in financial services.
method Survey analysis using New Institutional Economics and Dynamic Capabilities Theory.
result Experts expect adoption of DeFi to rise from negligible to 43% by 2034, with traditional finance likely to embrace it.
This paper tackles fair same-day delivery service by optimizing regional service rates.
problem Fairness in same-day delivery service for different neighborhoods.
method Partition service area into regions, use multi-objective Markov decision process and deep Q-learning.
result Our approach maximizes fairness across all regions while maintaining overall service rate.