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.
Real-time prediction of SLA violations in telecom services.
problem Detecting and predicting SLA violations in dynamic telecom environments.
method Online machine learning approach using streaming device-level metrics.
result Highly accurate predictions (>90% classification accuracy and < 10% false alarm rate).
Proposes a greedy algorithm for telecom offers to retain subscribers.
problem Maximizing revenue while preventing churn in telecom subscribers.
method Combinatorial algorithm for offer optimization under heterogeneous incentives.
result Efficient and accurate solution for large subscriber bases.
Study shows mobile money adoption varies by social groups in Pakistan.
problem Mobile money adoption patterns in Pakistan.
method Analysis of call detail records from a major telecom company.
result User mobility features are crucial for mobile money adoption.
Proposes a deep learning churn prediction system for telecom using TL and meta-classification.
problem Churn prediction challenges in telecom due to large data, high dimensions, and imbalanced data.
method Transfer Learning (TL) and Ensemble-based Meta-Classification. Two stages: TL on Deep CNNs, then GP-AdaBoost meta-classifier.
result TL-DeepE system achieved 75.4% and 68.2% prediction accuracy on Orange and Cell2cell datasets, respectively.
Predicts gender and age from mobile phone data for marketing.
problem Enhance marketing offers by predicting customer demographics.
method Machine learning algorithms applied to CDRs, CRM, and billing info.
result 85.6% accuracy in gender prediction, 65.5% in age prediction.
Detects BGP routing anomalies using bursty announcement patterns.
problem Detecting disruptive routing updates in the Internet.
method Burstiness measure of inter-arrival times around disruptive update dates and times.
result High recall and better precision in detecting anomalous incidents.
Model predicts telecom customer churn with high accuracy.
problem Predicting customers at risk of leaving telecom companies.
method Machine learning and social network analysis on big data platform.
result Model achieved 93.3% AUC, significantly improving churn prediction.
Pioneers a new network slicing solution for beyond-5G networks.
problem Ensuring diverse service level agreements (SLAs) in a multi-tenant environment with varying latency and throughput requirements.
method Leverages a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions based on system structure information.
result Near-optimal results in resource slicing decisions with no prior knowledge of traffic demand or channel quality statistics.
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.
Services contribute to economic growth by increasing complexity indices.
problem Understanding the role of services in economic growth and complexity.
method Combining network science and econometrics, analyzing data from developed and developing countries.
result Services have higher complexity indices than goods, indicating a potential route for economic growth.
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.
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.
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.
LogAnMeta detects anomalies from log events using meta learning.
problem Poor performance of current log anomaly detection on new or unseen anomalies.
method Meta-learning-based hybrid few-shot classifier trained in an episodic manner.
result Demonstrates efficacy of LogAnMeta on detecting anomalies with few samples.
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.
Monopoly breakup impacts AT&T and AMX's market value by 65% and 32%.
problem Understanding how financial markets value monopoly rents post-breakup.
method Univariate structural time series models to estimate pre-breakup and post-breakup values.
result Monopoly breakup significantly impacts market capitalization, as shown by AT&T and AMX.
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%.
The paper analyzes C.5 algorithm for customer segmentation in telecom industry.
problem Optimizing churners and minimizing marketing costs in competitive telecom industry.
method Use of C.5 algorithm within naive Bayesian modelling for customer segmentation.
result Experimental implementation of C.5 algorithm for customer profiling.
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.
Extracts patterns from mobile network data for better resource management.
problem Improving network efficiency and resource allocation for mobile users.
method Spatiotemporal analysis of internet activity records (IARs) data.
result Developed a mobile traffic partitioning scheme.
Web service embeds Wikidata entities for multilingual access.
problem Lack of easy access to Wikidata entities in various languages.
method Trained Word2Vec embeddings on Wikidata dump, exposed via REST API.
result Multilingual resource for over 600,000 Wikidata items and properties.
An online learning framework optimizes pricing and capacity in service systems.
problem Optimizing pricing and capacity in dynamic service systems.
method Gradient-based Online Learning in Queue (GOLiQ) framework.
result GOLiQ achieves logarithmic regret bound and improves service provider's performance.
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.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
problem Adaptation of users and services to reduce risk affects learning and performance.
method Analyzed a class of dynamics where users allocate participation and services update parameters.
result Repeated myopic updates with multiple learners lead to better outcomes than repeated risk minimization.
Digital financial services enhance firm performance through improved market share and profitability.
problem Insufficient academic attention to the impact of digital financial services on firm performance.
method Systematic review of literature, classification of methodologies, and identification of research gaps.
result Digital financial services significantly impact firm performance but have not received adequate academic attention.
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.
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.
Financial services need explainable AI for fair lending.
problem Challenges in credit decisioning due to compliance and ethical considerations.
method Use of explainable AI models to address fairness and ethics in lending.
result Explainable AI models improve fairness and ethics in lending decisions.
Verifies machine learning service claims to prevent cheating.
problem Fraudulent bypassing of expensive training procedures in CaaS.
method Probabilistic performance metrics, instance seeding, steganography, blackbox adversarial procedure, smart contract-based decentralized system.
result Designs a system to verify and incentivize service proofing and accountability.
Policy certifies inventory levels meeting service requirements.
problem Maintaining stock levels meeting service requirements despite unknown demand.
method Data-driven order policy using online learning and integral action.
result Valid inference method for finite samples.
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.
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.
Computable contracts simplify financial transactions and reduce legal costs.
problem Difficulty in querying, executing, and analyzing text-based financial contracts.
method Develop a Contract Definition Language and illustrate use cases.
result Substantial improvements in customer experience and cost reduction.
Paper introduces privacy-preserving deep-learning service.
problem Privacy loss in deep-learning-as-a-service.
method Homomorphic Encryption tailored for CNNs.
result Effectiveness of proposed privacy-preserving architecture.
Game theory helps analyze ESOs/EBIs in production and service sectors.
problem Economic incentives affect traditional production/service functions and create intangible capital.
method Uses game theory to analyze interactions in ESO/EBI transactions.
result No perfect Nash Equilibria for two-stage games involving many participants.
Privacy-preserving method protects user speech data from cloud services.
problem Privacy compromise in cloud-based speech analysis.
method Collects and sanitizes speech data before sharing, using transformation functions and voice conversion.
result Identification of sensitive emotional state reduced by ~96%.
Improved car-hailing service by analyzing POI effects.
problem Minimize passenger waiting time and optimize vehicle utilization.
method Analyzed POI effects on supply-demand gap and proposed a POI selection scheme integrated with XGBoost.
result More accurate and stable estimation results.
Study analyzes mobile money adoption in 3 cultures, finds context-dependent models.
problem Identifying individuals most likely to benefit from mobile money adoption.
method Combining terabyte-scale data from Ghana, Pakistan, and Zambia to develop supervised learning models.
result Models fit on one population do not generalize to another, highlighting context-dependency.
The writers propose a mathematical Method for deriving risk weights which describe how a borrower's income, relative to their debt service obligations (serviceability) affects the probability of default of the loan. The Method considers the borrower's income not simply as a known quantity at the time the loan is made, …
MLCapsule protects sensitive data by executing models locally.
problem Protecting sensitive data and model security in MLaaS.
method Local execution of models on client side, coupled with security measures.
result Models executed locally without data leaving the client, protecting against attacks.
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.
LightAutoML automates ML for a large financial services company.
problem Building high-quality ML models for a complex financial ecosystem.
method Developed an AutoML system tailored to a large European financial services company's unique requirements.
result LightAutoML outperformed experienced data scientists and other open-source solutions.
Graph learning categorizes DeFi services into similar functionalities.
problem Identifying similar financial services in decentralized finance protocols.
method Graph representation learning (GRL) to categorize smart contract blocks into clusters.
result Purity of clustering reaches .888 in the best-case scenario.
AI enhances financial services but humans are irreplaceable for empathy, presence, and ethics.
problem AI's limitations in financial services, especially with small datasets and human judgment.
method EPOCH framework highlighting five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope.
result Humans are essential for trust, innovation, and consumer experience in financial services.
GOLOMB improves dialogue state tracking for unseen services.
problem Improving dialogue state tracking for multiple services and APIs.
method GOLOMB uses a BERT-based model that queries dialogue history with slot descriptions and values.
result GOLOMB achieves a joint goal accuracy of 53.97% on the SGD dataset.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
The professional services sector is at a turning point, with some industries showing growth opportunities.
problem Identifying growth opportunities in the professional services sector after decades of growth.
method A simple framework applied to the US economic context to diagnose growth opportunities.
result The professional services sector is expected to stall at a national level, but some industries still offer growth opportunities.