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- research-articleApril 2024
TG-SPRED: Temporal Graph for Sensorial Data PREDiction
ACM Transactions on Sensor Networks (TOSN), Volume 20, Issue 3Article No.: 64, Pages 1–20https://doi.org/10.1145/3649892This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readings ...
- ArticleSeptember 2023
- research-articleFebruary 2023
A Secure Intelligent System for Internet of Vehicles: Case Study on Traffic Forecasting
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 11Nov. 2023, Pages 13218–13227https://doi.org/10.1109/TITS.2023.3243542Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV models ...
- research-articleOctober 2022
Group intrusion detection in the Internet of Things using a hybrid recurrent neural network
Cluster Computing (KLU-CLUS), Volume 26, Issue 2Apr 2023, Pages 1147–1158https://doi.org/10.1007/s10586-022-03779-wAbstractThis paper introduces a novel framework for identifying a group of intrusions in the context of the Internet of Things (IoT). It combines both deep learning and decomposition. A set of data is first collected and a recurrent neural network is used ...
- rapid-communicationJune 2022
Vehicle detection using improved region convolution neural network for accident prevention in smart roads
Pattern Recognition Letters (PTRL), Volume 158, Issue CJun 2022, Pages 42–47https://doi.org/10.1016/j.patrec.2022.04.012Highlights- We develop a novel cleaning algorithm to remove the unnecessary features based on SIFT extractor.
This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the ...
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- research-articleSeptember 2021
Towards Optimized One-Step Clustering Approach in Wireless Sensor Networks
Wireless Personal Communications: An International Journal (WPCO), Volume 120, Issue 2Sep 2021, Pages 1501–1523https://doi.org/10.1007/s11277-021-08521-0AbstractThis paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as ...
- research-articleJuly 2021
A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 22, Issue 7July 2021, Pages 4496–4506https://doi.org/10.1109/TITS.2020.3022612This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a <italic>two phase-based algorithm</italic>. The ...
- research-articleApril 2021
Cluster-based information retrieval using pattern mining
Applied Intelligence (KLU-APIN), Volume 51, Issue 4Apr 2021, Pages 1888–1903https://doi.org/10.1007/s10489-020-01922-xAbstractThis paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information ...
- research-articleFebruary 2021
Trajectory Outlier Detection: New Problems and Solutions for Smart Cities
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 2Article No.: 20, Pages 1–28https://doi.org/10.1145/3425867This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the ...
- research-articleJanuary 2021
Machine Learning for Identifying Group Trajectory Outliers
ACM Transactions on Management Information Systems (TMIS), Volume 12, Issue 2Article No.: 12, Pages 1–25https://doi.org/10.1145/3430195Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the ...
- research-articleOctober 2020
- research-articleOctober 2020
A recurrent neural network for urban long-term traffic flow forecasting
Applied Intelligence (KLU-APIN), Volume 50, Issue 10Oct 2020, Pages 3252–3265https://doi.org/10.1007/s10489-020-01716-1AbstractThis paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, ...
- research-articleOctober 2019
Wireless energy efficient occupancy-monitoring system for smart buildings
Pervasive and Mobile Computing (PAMC), Volume 59, Issue COct 2019https://doi.org/10.1016/j.pmcj.2019.101037AbstractRationalizing energy consumption in smart buildings is considered in this paper, and a wireless monitoring system based on Passive Infrared sensors (PIRs) is proposed. The proposed system is pervasive and can be integrated in existing ...
- research-articleSeptember 2019
Exploiting GPU and cluster parallelism in single scan frequent itemset mining
Information Sciences: an International Journal (ISCI), Volume 496, Issue CSep 2019, Pages 363–377https://doi.org/10.1016/j.ins.2018.07.020AbstractThis paper considers discovering frequent itemsets in transactional databases and addresses the time complexity problem by using high performance computing (HPC). Three HPC versions of the Single Scan (SS) algorithm are proposed. The ...
- research-articleSeptember 2019
Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases
- Youcef Djenouri,
- Djamel Djenouri,
- Asma Belhadi,
- Philippe Fournier-Viger,
- Jerry Chun-Wei Lin,
- Ahcene Bendjoudi
Information Sciences: an International Journal (ISCI), Volume 496, Issue CSep 2019, Pages 326–342https://doi.org/10.1016/j.ins.2018.06.060AbstractThis paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, ...
- research-articleJune 2019
Single Scan Polynomial Algorithms for Frequent Itemset Mining in Big Databases
2019 IEEE Congress on Evolutionary Computation (CEC)Jun 2019, Pages 1453–1460https://doi.org/10.1109/CEC.2019.8790127This paper considers frequent itemset mining in big transactional databases. It first introduces a novel approach (Bio-SS) that combines the bio-inspired algorithms with the single scan algorithm (SSFIM). The proposed approach addresses the limitations of ...
- research-articleJune 2019
A Novel Parallel Framework for Metaheuristic-based Frequent Itemset Mining
- Youcef Djenouri,
- Djamel Djenouri,
- Asma Belhadi,
- Jerry Chun-Wei Lin,
- Ahcene Bendjoudi,
- Philippe Fournier-Viger
2019 IEEE Congress on Evolutionary Computation (CEC)Jun 2019, Pages 1439–1445https://doi.org/10.1109/CEC.2019.8790116Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent years to ...
- ArticleMay 2019
IoT-DMCP: An IoT Data Management and Control Platform for Smart Cities
CLOSER 2019: Proceedings of the 9th International Conference on Cloud Computing and Services ScienceMay 2019, Pages 578–583https://doi.org/10.5220/0007861005780583This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and ...
- articleMay 2019
Bee swarm optimization for solving the MAXSAT problem using prior knowledge
Soft Computing - A Fusion of Foundations, Methodologies and Applications (SOFC), Volume 23, Issue 9May 2019, Pages 3095–3112https://doi.org/10.1007/s00500-017-2956-1This paper explores rule decomposition for solving the MAXSAT problem. Four approaches are proposed to steer a bee swarm optimization metaheuristic. Two decomposition methods are proposed: direct and indirect. The first one applies the Kmeans algorithm, ...
- surveyMarch 2019
Machine Learning for Smart Building Applications: Review and Taxonomy
ACM Computing Surveys (CSUR), Volume 52, Issue 2Article No.: 24, Pages 1–36https://doi.org/10.1145/3311950The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects ...