@article {Talei2022,
	title = {Correction: Talei et al. Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning. Energies 2021, 14, 6042},
	journal = {Energies},
	volume = {15},
	number = {9},
	year = {2022},
	note = {cited By 0},
	doi = {10.3390/en15093191},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129777741\&doi=10.3390\%2fen15093191\&partnerID=40\&md5=38e34eaba94b4e84edf5500b90f68af8},
	author = {Talei, H. and Benhaddou, D. and Gamarra, C. and Benbrahim, H. and Essaaidi, M.}
}
@article {Adil2022,
	title = {COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study},
	journal = {Biology},
	volume = {11},
	number = {8},
	year = {2022},
	note = {cited By 1},
	abstract = {The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used. {\textcopyright} 2022 by the authors.},
	doi = {10.3390/biology11081221},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137364249\&doi=10.3390\%2fbiology11081221\&partnerID=40\&md5=1602d312118c2b7347846773ddd73594},
	author = {Adil, B. and Lhazmir, S. and Ghogho, M. and Benbrahim, H.}
}
@article {Echihabi20222005,
	title = {Hercules Against Data Series Similarity Search},
	journal = {Proceedings of the VLDB Endowment},
	volume = {15},
	number = {10},
	year = {2022},
	note = {cited By 3},
	pages = {2005-2018},
	abstract = {We propose Hercules, a parallel tree-based technique for exact similarity search on massive disk-based data series collections. We present novel index construction and query answering algorithms that leverage different summarization techniques, carefully schedule costly operations, optimize memory and disk accesses, and exploit the multi-threading and SIMD capabilities of modern hardware to perform CPU-intensive calculations. We demonstrate the superiority and robustness of Hercules with an extensive experimental evaluation against state-of-the-art techniques, using many synthetic and real datasets, and query workloads of varying difficulty. The results show that Hercules performs up to one order of magnitude faster than the best competitor (which is not always the same). Moreover, Hercules is the only index that outperforms the optimized scan on all scenarios, including the hard query workloads on disk-based datasets. {\textcopyright} 2022, VLDB Endowment., All rights reserved.},
	keywords = {CPU-intensive, Data series, Disk-based, Exact similarity searches, Experimental evaluation, Index construction, Multi-threading, Query answering, Similarity search, Tree-based, Trees (mathematics)},
	doi = {10.14778/3547305.3547308},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132784935\&doi=10.14778\%2f3547305.3547308\&partnerID=40\&md5=539332f4b18822ebf450dd3c3f5fda5d},
	author = {Echihabi, K. and Fatourou, P. and Zoumpatianos, K. and Palpanas, T. and Benbrahim, H.}
}
@article {Hammouchi2022,
	title = {STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2022},
	note = {cited By 0},
	abstract = {Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose a STRisk predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve an Area Under Curve (AUC) score exceeding 98\%, which is 12\% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors. IEEE},
	keywords = {Blogs, Cybe risk assessment, Cybe risk prediction, Data breach, Forecasting, Hacking breach, Learning systems, Malware, Noise measurements, Personal computing, Predictive models, Risk assessment, Risk predictions, Risks assessments, Security, Social networking (online)},
	doi = {10.1109/TDSC.2022.3149208},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124712380\&doi=10.1109\%2fTDSC.2022.3149208\&partnerID=40\&md5=f6b798bf0fc97476d3214d3efbc3597a},
	author = {Hammouchi, H. and Nejjari, N. and Mezzour, G. and Ghogho, M. and Benbrahim, H.}
}
@conference {Nejjari2021,
	title = {Conflict spectrum: An empirical study of geopolitical cyber threats from a social network perspective},
	booktitle = {2021 8th International Conference on Social Network Analysis, Management and Security, SNAMS 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {The current evolution of the cyber-threat ecosystem shows that no state or organization is safe from cyber threats. It is therefore important to understand factors that may influence cyber risk. In this paper, we highlight the geopolitical context of cyber threats. The key idea is to verify the assumption that cyber threats are correlated to geopolitical events. We use social network techniques to model our problem as a network. We investigate the relationship between cyber threats and geopolitical events through the measure of similarity between two graphs using the QAP-correlation method. To verify this assumption empirically, we use data from the GDELT project. The results of QAP-correlation measure show a significant similarity between the cyber events graph and the geopolitical events graph in terms of graph isomorphism and structure. {\textcopyright} 2021 IEEE.},
	keywords = {{\textquoteright}current, Cyber threats, Empirical studies, Event graphs, Geopolitical events, QAP-correlation, Similarity, Social Network Analysis, Spectra{\textquoteright}s, Use social networks},
	doi = {10.1109/SNAMS53716.2021.9732155},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127404986\&doi=10.1109\%2fSNAMS53716.2021.9732155\&partnerID=40\&md5=d81465c991f5d449e0db9613f3c31aa7},
	author = {Nejjari, N. and Lahlou, S. and Fadi, O. and Zkik, K. and Oudani, M. and Benbrahim, H.}
}
@article {Houmz2021,
	title = {Detecting the impact of software vulnerability on attacks: A case study of network telescope scans},
	journal = {Journal of Network and Computer Applications},
	volume = {195},
	year = {2021},
	note = {cited By 1},
	abstract = {Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85\% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities{\textquoteright} impact on scans. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {Case-studies, Classification algorithm, CVE, Forecasting, Machine learning, Network scan, Network scanning, Network security, Network telescopes, NVD, OR-networks, Scanning, Software vulnerabilities, Telescopes, Times series},
	doi = {10.1016/j.jnca.2021.103230},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116335984\&doi=10.1016\%2fj.jnca.2021.103230\&partnerID=40\&md5=1d7f8d5d4111761b27fc93badc70f925},
	author = {Houmz, A. and Mezzour, G. and Zkik, K. and Ghogho, M. and Benbrahim, H.}
}
@article {ElAkrouchi2021,
	title = {End-to-end LDA-based automatic weak signal detection in web news},
	journal = {Knowledge-Based Systems},
	volume = {212},
	year = {2021},
	note = {cited By 9},
	abstract = {An extremely competitive business environment requires every company to monitor its competitors and anticipate future opportunities and risks, creating a dire need for competitive intelligence. In response to this need, foresight study became a prominent field, especially the concept of weak signal detection. This research area has been widely studied for its utility, but it is limited by the need of human expert judgments on these signals. Moreover, the increase in the volume of information on the Internet through blogs and web news has made the detection process difficult, which has created a need for automation. Recent studies have attempted topic modeling techniques, specifically latent Dirichlet allocation (LDA), for automating the weak signal detection process; however, these approaches do not cover all parts of the process. In this study, we propose a fully automatic LDA-based weak signal detection method, consisting of two filtering functions: the weakness function aimed at filtering topics, which potentially contains weak signals, and the potential warning function, which helps to extract only early warning signs from the previously filtered topics. We took this approach with a famous daily web news dataset, and we could detect the risk of the COVID19 pandemic at an early stage. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Competition, Competitive business, Competitive intelligence, Detection process, Early warning signs, Filtering functions, Latent dirichlet allocations, Signal detection, Statistics, Topic Modeling, Weak signal detection, Weak signals},
	doi = {10.1016/j.knosys.2020.106650},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097573309\&doi=10.1016\%2fj.knosys.2020.106650\&partnerID=40\&md5=a491fca334a7b047c3a02e16ecb7b3f0},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@conference {Cherqi2021,
	title = {Leveraging Open Threat Exchange (OTX) to Understand Spatiooral Trends of Cyber Threats: Covid-19 Case Study},
	booktitle = {Proceedings - 2021 IEEE International Conference on Intelligence and Security Informatics, ISI 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Understanding the properties exhibited by Spatialoral evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks{\textquoteright} spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading. {\textcopyright} 2021 IEEE.},
	keywords = {Attack analysis, Case-studies, Computer viruses, COVID-19, Crime, Cybe attack analyse, Cybe threat forecasting, Cyber attacks, Cyber threats, Cyber-attacks, Cyber-crimes, Information analysis, Network security, Spatialoral data analyse for cybe-crime analyse, Threat intelligence, Viruses},
	doi = {10.1109/ISI53945.2021.9624677},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123484404\&doi=10.1109\%2fISI53945.2021.9624677\&partnerID=40\&md5=729113fca1f2d9817bbaa46a6f5d0036},
	author = {Cherqi, O. and Hammouchi, H. and Ghogho, M. and Benbrahim, H.}
}
@conference {Walid2021,
	title = {Modeling and optimization of anaerobic digestion: A review},
	booktitle = {E3S Web of Conferences},
	volume = {229},
	year = {2021},
	note = {cited By 2},
	abstract = {Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models. {\textcopyright} The Authors, published by EDP Sciences, 2021.},
	keywords = {Anaerobic digestion, Biogas production, Data-driven model, Machine learning models, Management techniques, Meta-heuristic techniques, Modeling and optimization, Monitoring parameters, Optimization, Renewable energies},
	doi = {10.1051/e3sconf/202122901022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100132508\&doi=10.1051\%2fe3sconf\%2f202122901022\&partnerID=40\&md5=5d2367868bbe9ebe8e867ca9622eda34},
	author = {Walid, F. and El Fkihi, S. and Benbrahim, H. and Tagemouati, H.}
}
@article {Abou-zbiba2021311,
	title = {A Novel Mobile CrowdSensing Architecture for Road Safety},
	journal = {Lecture Notes in Networks and Systems},
	volume = {183},
	year = {2021},
	note = {cited By 0},
	pages = {311-324},
	abstract = {Intelligent Transportation Systems have become an essential part of today{\textquoteright}s transportation systems as they aim to enhance efficiency, safety and mobility. They rely particularly on various communication and sensing technologies to achieve their objectives. At this level, Mobile CrowdSensing presents a cost-efficient solution and provides interesting features for data collection which is a major component in ITS. However, it still faces some challenges such as lack of incentive mechanisms, data validation, privacy and security. These challenges motivate us to propose a Mobile CrowdSensing architecture for our future SI-CAR (Secure and Intelligent Crowdsensing Application for Road Safety) application that integrates deep learning-based data validation, edge computing-based local processing for data privacy and gamification based-incentive mechanism. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-66840-2_24},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102625921\&doi=10.1007\%2f978-3-030-66840-2_24\&partnerID=40\&md5=625844bcb30cf1f884057c1415e851e2},
	author = {Abou-zbiba, W. and El Gadi, H. and El Bakkali, H. and Benbrahim, H. and Benhaddou, D.}
}
@conference {ElAkrouchi2021,
	title = {Review on adopting concept extraction in weak signals detection in competitive intelligence},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2021},
	note = {cited By 0},
	abstract = {The dynamic nature of competition in the business environment makes a company{\textquoteright}s ability to secure future change more critical to its survival. Consequently, efficient exploitation of valuable intel is globally acknowledged as an essential foundation of competitive advantage, leading to Competitive Intelligence. Besides, one of the crucial keys to successful competitive information securing is studying the future. Thus, predicting what may happen in the uncertain future is a leading-edge technology leading to an extensive need for foresight analysis. Foresight study uses various methods to recognize future developments and make plans that anticipate possible future changes. One of the leading techniques used in foresight is detecting and understanding Weak Signals. But knowing the nature of these signals, automatically scanning them is still considered a difficult task. For this, we examine the Concept Extraction technique as a main step to detect weak signals from documents automatically. In this paper, we will explain the concept extraction methods used so far, and we present in detail all the main methods and approaches and their application in detecting weak signals. {\textcopyright} 2021 Association for Computing Machinery.},
	keywords = {Business environments, Competition, Competitive advantage, Competitive intelligence, Concept extraction, Data mining, Dynamic nature, Extraction, Leading edge technology, Possible futures, Signal detection, Topic Modeling, Weak signal detection, Weak signals},
	doi = {10.1145/3485557.3485560},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121505750\&doi=10.1145\%2f3485557.3485560\&partnerID=40\&md5=01161f382ecb38a8faf2883a2bc37003},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@article {Talei2021,
	title = {Smart building energy inefficiencies detection through time series analysis and unsupervised machine learning},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	note = {cited By 6},
	abstract = {The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building{\textquoteright}s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6\% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Clusterings, Conditioning systems, energy efficiency, Energy management systems, Energy savings, Energy utilization, Energy-savings, Harmonic analysis, Heating ventilation and air conditioning, Houston, HVAC, Internet of things, K-means clustering, Leadership in energy and environment design building, Leadership in energy and environment designs, Learning algorithms, Office buildings, Potential energy, Time series analysis, Time-series analysis, Times series, Tropics, Unsupervised learning},
	doi = {10.3390/en14196042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115608885\&doi=10.3390\%2fen14196042\&partnerID=40\&md5=e01e465020f683b8a7ccebf61bf08077},
	author = {Talei, H. and Benhaddou, D. and Gamarra, C. and Benbrahim, H. and Essaaidi, M.}
}
@conference {ElAkrouchi2020,
	title = {Monitoring Early Warning Signs Evolution through Time},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	abstract = {In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts{\textquoteright} opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals{\textquoteright} evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis. {\textcopyright} 2020 ACM.},
	keywords = {Artificial intelligence, Business competition, Competition, Early warning signs, Financial crisis, Possible futures, Semantics, Signal detection, Warning signs, Weak signals},
	doi = {10.1145/3446132.3446173},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102970850\&doi=10.1145\%2f3446132.3446173\&partnerID=40\&md5=4f35fa04a194332457fca88ff3bec0d1},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@article {Echihabi2020402,
	title = {Return of the lernaean hydra: Experimental evaluation of data series approximate similarity search},
	journal = {Proceedings of the VLDB Endowment},
	volume = {13},
	number = {3},
	year = {2020},
	note = {cited By 25},
	pages = {402-419},
	abstract = {Data series are a special type of multidimensional data present in numerous domains, where similarity search is a key operation that has been extensively studied in the data series literature. In parallel, the multidimensional community has studied approximate similarity search techniques. We propose a taxonomy of similarity search techniques that reconciles the terminology used in these two domains, we describe modifications to data series indexing techniques enabling them to answer approximate similarity queries with quality guarantees, and we conduct a thorough experimental evaluation to compare approximate similarity search techniques under a unified framework, on synthetic and real datasets in memory and on disk. Although data series differ from generic multidimensional vectors (series usually exhibit correlation between neighboring values), our results show that data series techniques answer approximate queries with strong guarantees and an excellent empirical performance, on data series and vectors alike. These techniques outperform the state-of-the-art approximate techniques for vectors when operating on disk, and remain competitive in memory.},
	keywords = {Approximate query, Empirical performance, Experimental evaluation, Indexing techniques, Multi-dimensional vectors, Multidimensional data, Quality control, Query processing, Similarity search, Unified framework},
	doi = {10.14778/3368289.3368303},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092075628\&doi=10.14778\%2f3368289.3368303\&partnerID=40\&md5=c84591d6c7c3001ccc91a3584e71e4ff},
	author = {Echihabi, K. and Zoumpatianos, K. and Palpanas, T. and Benbrahim, H.}
}
@article {Rezzouqi20181145,
	title = {Analyzing the accuracy of historical average for urban traffic forecasting using google maps},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {868},
	year = {2018},
	pages = {1145-1156},
	doi = {10.1007/978-3-030-01054-6_79},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057079293\&doi=10.1007\%2f978-3-030-01054-6_79\&partnerID=40\&md5=5c985c898807cd5eb68ed52c62ea6335},
	author = {Rezzouqi, H. and Gryech, I. and Sbihi, N. and Ghogho, M. and Benbrahim, H.}
}
@article {Fenjiro201820,
	title = {Deep reinforcement learning overview of the state of the art},
	journal = {Journal of Automation, Mobile Robotics and Intelligent Systems},
	volume = {12},
	number = {3},
	year = {2018},
	pages = {20-39},
	doi = {10.14313/JAMRIS_3-2018/15},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060533274\&doi=10.14313\%2fJAMRIS_3-2018\%2f15\&partnerID=40\&md5=179cf6788e2041bdac84079f25c37a3e},
	author = {Fenjiro, Y. and Benbrahim, H.}
}
@conference {Echihabi2018112,
	title = {The lernaean hydra of data series similarity search: An experimental evaluation of the state of the art},
	booktitle = {Proceedings of the VLDB Endowment},
	volume = {12},
	number = {2},
	year = {2018},
	pages = {112-127},
	doi = {10.14778/3282495.3282498},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061729182\&doi=10.14778\%2f3282495.3282498\&partnerID=40\&md5=1b5df3c860cb0457dac9c0facb8d6f52},
	author = {Echihabi, K. and Zoumpatianos, K. and Palpanas, T. and Benbrahim, H.}
}
@conference {Lahlou201864,
	title = {Textual context aware factorization machines: Improving recommendation by leveraging users{\textquoteright} reviews},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	pages = {64-69},
	doi = {10.1145/3289100.3289111},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058629650\&doi=10.1145\%2f3289100.3289111\&partnerID=40\&md5=c2869c20ec04d08f675efb27e1536863},
	author = {Lahlou, F.Z. and Benbrahim, H. and Kassou, I.}
}
@conference {Lahlou20163312,
	title = {Improving recommendations using context from users{\textquoteright} reviews},
	booktitle = {Proceedings of the 27th International Business Information Management Association Conference - Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA 2016},
	year = {2016},
	note = {cited By 0},
	pages = {3312-3315},
	abstract = {Recommender systems enable E-commerce websites to improve their sales by suggesting relevant items to customers. If considering the context of buying situations, more accurate recommendations can be computed. In this paper, we discuss the issue of using the contextual information contained in users{\textquoteright} reviews in order to improve recommendation. Copyright {\textcopyright} 2016 International Business Information Management Association},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984653667\&partnerID=40\&md5=a03c657d3c2b4702850bc5f2e897eb3a},
	author = {Lahlou, F.Z. and Benbrahim, H. and Kassou, I.}
}
@conference {Amarouche20162876,
	title = {Introduction to competitive intelligence: Process, applications and tools},
	booktitle = {Proceedings of the 27th International Business Information Management Association Conference - Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA 2016},
	year = {2016},
	note = {cited By 0},
	pages = {2876-2885},
	abstract = {In the world of excessive business competitiveness, almost every company tries to monitor its environment to exceed the competitors. Getting knowledge about competitors is the basic principal of what is called Competitive Intelligence (CI). Many applications of Competitive Intelligence can be used like Opinion Mining and Foresight studies and the process of obtaining such intelligence differs according to the company{\textquoteright}s needs. In this paper, we will present in more details the definitions of CI and a general process grouping the most used steps in conducting such study. In the end we will present some tools useful in CI. Copyright {\textcopyright} 2016 International Business Information Management Association},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984646253\&partnerID=40\&md5=1c66c097dc1b53d274daa126d36a743f},
	author = {Amarouche, K. and El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@article {Mountassir201664,
	title = {New under-sampling methods to address the problem of unbalanced sentiment classification: Application on Arabic datasets},
	journal = {International Journal of Information and Communication Technology},
	volume = {9},
	number = {1},
	year = {2016},
	note = {cited By 0},
	pages = {64-77},
	abstract = {This paper presents the study we have carried out to address the problem of unbalanced datasets in supervised sentiment classification in an Arabic context. We propose three different methods to under-sample the majority class documents. Our goal is to compare the effectiveness of the proposed methods with the common random under-sampling. We also aim to evaluate the behaviour of the classifier toward different under-sampling rates. We use three different common classifiers, namely Na{\"\i}ve Bayes, support vector machines and k-nearest neighbours. The experiments are carried out on two different Arabic datasets that we have built internally. We show that results obtained on the first dataset, which is slightly skewed, are better than those obtained on the second one which is highly skewed. We conclude also that Na{\"\i}ve Bayes is sensitive to dataset size, the more we reduce the data the more the results degrade. However, support vector machines are highly sensitive to unbalanced datasets. We record an instable behaviour of k-nearest neighbour. The results show also that we can rely on the proposed techniques and that they are typically competitive with random under-sampling. {\textcopyright} 2016 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJICT.2016.077687},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979641121\&doi=10.1504\%2fIJICT.2016.077687\&partnerID=40\&md5=3261431f79a2a1c6ae94fa16e8ad8b01},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Amarouche2015358,
	title = {Product Opinion Mining for Competitive Intelligence},
	booktitle = {Procedia Computer Science},
	volume = {73},
	year = {2015},
	note = {cited By 1},
	pages = {358-365},
	abstract = {Competitive Intelligence is one of the keys of companies Risk Management. It provides the company with a permanent lighting to its competitive environment. The increasingly frequent use of Information and Communication Technologies (ICT); including (namely) online shopping sites, blogs, social network sites, forums, provides incentives for companies check their advantages over their competitors. This information presents a new source that helps and leads the company to identify, analyze and manage the various risks associated with its business/products. Nowadays, a good use of these data helps the company to improve its products/services. In this paper, an overview of opinion mining for competitive intelligence will be presented. We{\textquoteright}ll try to synthesize the major research done for the different steps of product opinion mining. {\textcopyright} 2015 The Authors.},
	doi = {10.1016/j.procs.2015.12.004},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962678879\&doi=10.1016\%2fj.procs.2015.12.004\&partnerID=40\&md5=6d1bc9b3ded99bfaf39408391296ee1d},
	author = {Amarouche, K. and Benbrahim, H. and Kassou, I.}
}
@article {Mountassir2014442,
	title = {The nearest centroid based on vector norms: A new classification algorithm for a new document representation model},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8556 LNAI},
	year = {2014},
	note = {cited By 1},
	pages = {442-456},
	abstract = {In this paper, we present a novel model that we propose for document representation. In contrast with the classical Vector Space Model which represents each document by a unique vector in the feature space, our model consists in representing each document by a vector in the space of training documents of each category. We develop, for this novel model, a discriminative classifier which is based on the norms of the generated vectors by our model. We call this algorithm the Nearest Cetroid based on Vector Norms. Our major goal, by the proposition of such new classification framework, is to overcome the problems related to huge dimensionality and vector sparsity which are commonly faced in Text Classification problems. We evaluate the performance of the proposed framework by comparing its effectiveness and efficiency with those of some standard classifiers when used with the classical document representation. The studied classifiers are Na{\"\i}ve Bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We conduct our experiments on multi-lingual balanced and unbalanced binary data sets. Our results show that our algorithm typically performs well since it is competitive with the classical methods and, at the same time, dramatically faster especially in comparison with NB and kNN. We also apply our model on the Reuters21578 corpus so as to evaluate its performance in a multi-class environment. We can say that the obtained result (85.4\% in terms of micro-F1) is promising and that it can be improved in future works. {\textcopyright} 2014 Springer International Publishing Switzerland.},
	doi = {10.1007/978-3-319-08979-9_34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958526350\&doi=10.1007\%2f978-3-319-08979-9_34\&partnerID=40\&md5=a5d3d5cd03b740999c7e4bcf28e33e1a},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir201330,
	title = {Representing text documents in training document spaces: A novel model for document representation},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {56},
	number = {1},
	year = {2013},
	note = {cited By 0},
	pages = {30-39},
	abstract = {In this paper, we propose a novel model for Document Representation in an attempt to address the problem of huge dimensionality and vector sparseness that are commonly faced in Text Classification tasks. The proposed model consists of representing text documents in the space of training documents at a first stage. Afterward, the generated vectors are projected in a new space where the number of dimensions corresponds to the number of categories. To evaluate the effectiveness of our model, we focus on a problem of binary classification. We conduct our experiments on Arabic and English data sets of Opinion Mining. We use as classifiers Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) which are known by their effectiveness in classical Text Classification tasks. We compare the performance of our model with that of the classical Vector Space Model (VSM) by the consideration of three evaluative criteria, namely dimensionality of the generated vectors, time (of learning and testing) taken by the classifiers, and classification results in terms of accuracy. Our experiments show that the effectiveness of our model (in comparison with the classical VSM) depends on the used classifier. Results yielded by k-NN when applying our model are better or as those obtained when applying the classical VSM. For SVM, results yielded when applying our model are in general, slightly lower than those obtained when using VSM. However, the gain in terms of time and dimensionality reduction is so promising since they are dramatically decreased by the application of our model. {\textcopyright} 2005 - 2013 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885352218\&partnerID=40\&md5=21216dd814d8bc9aa5e4bfb7a08e6be8},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir201373,
	title = {Sentiment classification on arabic corpora: A preliminary cross-study},
	journal = {Document Numerique},
	volume = {16},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {73-96},
	abstract = {The rise of social media (such as online web forums and social networking sites) has attracted interests to mining and analyzing opinions available on the web. The online opinion has become the object of studies in many research areas; especially that called "Opinion Mining and Sentiment Analysis". Several interesting and advanced works were performed on few languages (in particular English). However, there were very few studies on Morphologically Rich Languages such as Arabic. This paper presents the study we have carried out to investigate supervised sentiment classification in an Arabic context. We use two Arabic Corpora which are different in many aspects. We use three common classifiers known by their effectiveness, namely Na{\"\i}ve Bayes, Support Vector Machines and k-Nearest Neighbor. We investigate some settings to identify those that allow achieving the best results. These settings are about stemming type, term frequency thresholding, term weighting and ngram words. We show that Na{\"\i}ve Bayes and Support Vector Machines are competitively effective; however k-Nearest Neighbor{\textquoteright}s effectiveness depends on the corpus. Through this study, we recommend to use light-stemming rather than stemming, to remove terms that occur once, to combine unigram and bigram words and to use presence-based weighting rather than frequency-based one. Our results show also that classification performance can be influenced by documents length, documents homogeneity and the nature of document authors. However, the size of data sets does not have an impact on classification results. {\textcopyright} 2013 Lavoisier.},
	doi = {10.3166/DN.16.1.73-96},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879022187\&doi=10.3166\%2fDN.16.1.73-96\&partnerID=40\&md5=203c5325261be58119451ab15e8141b5},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Lahlou2013,
	title = {A Text Classification based method for context extraction from online reviews},
	booktitle = {2013 8th International Conference on Intelligent Systems: Theories and Applications, SITA 2013},
	year = {2013},
	note = {cited By 1},
	abstract = {Recommender systems are systems that filter information depending on users{\textquoteright} profiles and suggest items that might match their preferences. While the majority of existing researches compute recommendation by considering only users and items, Context Aware Recommendation Systems (CARS) consider, in addition to users and items, others features related to the context. A first issue in CARS studies is to identify the contextual features. In this paper, we investigate the use of Text Classification techniques to extract contextual features from users{\textquoteright} reviews. We conduct experiments to identify the best classification algorithm for our dataset. We evaluate our approach on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Multinomial Na{\"\i}ve Bayes performs best in our dataset, with a F1 score of 60.1\%. Since contextual information are not always provided in the reviews, we think that our results are promising. We conclude that this research area needs deeper studies. {\textcopyright} 2013 IEEE.},
	doi = {10.1109/SITA.2013.6560804},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883074857\&doi=10.1109\%2fSITA.2013.6560804\&partnerID=40\&md5=440fb81da26cacbb428c326c7dc6cf50},
	author = {Lahlou, F.Z. and Mountassir, A. and Benbrahim, H. and Kassou, I.}
}
@conference {Mountassir2012306,
	title = {Addressing the problem of unbalanced data sets in sentiment analysis},
	booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},
	year = {2012},
	note = {cited By 0},
	pages = {306-311},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881521979\&partnerID=40\&md5=c7a7678cb1231468ccf73bfed1974063},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir2012259,
	title = {A cross-study of sentiment classification on arabic corpora},
	booktitle = {Res. and Dev. in Intelligent Syst. XXIX: Incorporating Applications and Innovations in Intel. Sys. XX - AI 2012, 32nd SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.},
	year = {2012},
	note = {cited By 5},
	pages = {259-272},
	doi = {10.1007/978-1-4471-4739-8-21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881507047\&doi=10.1007\%2f978-1-4471-4739-8-21\&partnerID=40\&md5=558cbf80cf3c4c368bae6571c00c9274},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir20123298,
	title = {An empirical study to address the problem of unbalanced data sets in sentiment classification},
	booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
	year = {2012},
	note = {cited By 7},
	pages = {3298-3303},
	doi = {10.1109/ICSMC.2012.6378300},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872383401\&doi=10.1109\%2fICSMC.2012.6378300\&partnerID=40\&md5=7e29a74d448b1a5da8a64fa5e3a0063b},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir201243,
	title = {Some methods to address the problem of unbalanced sentiment classification in an arabic context},
	booktitle = {CiSt 2012 - Proceedings: 2012 Colloquium in Information Science and Technology},
	year = {2012},
	note = {cited By 2},
	pages = {43-48},
	doi = {10.1109/CIST.2012.6388061},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872535822\&doi=10.1109\%2fCIST.2012.6388061\&partnerID=40\&md5=81312470e1e0a89c333acbcd558540a2},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Benbrahim2011127,
	title = {Fuzzy Semi-supervised support vector machines},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6871 LNAI},
	year = {2011},
	note = {cited By 0},
	pages = {127-139},
	doi = {10.1007/978-3-642-23199-5_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052329688\&doi=10.1007\%2f978-3-642-23199-5_10\&partnerID=40\&md5=ad21e53cfdf4a3fde7623eab57ebc543},
	author = {Benbrahim, H.}
}
