The k-means algorithm: A comprehensive survey and performance evaluation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions

Critical Role of Artificially Intelligent Conversational Chatbot

Artificially intelligent chatbot, such as ChatGPT, represents a recent and powerful advancement in the AI domain. Users prefer them for obtaining quick and precise answers, avoiding the usual hassle of clicking through multiple links in traditional searches. ChatGPT's conversational approach makes it comfortable and accessible for finding answers quickly and in an organized manner. However, it is important to note that these chatbots have limitations, especially in terms of providing accurate answers as well as ethical concerns. In this study, we explore various scenarios involving ChatGPT's ethical implications within academic contexts, its limitations, and the potential misuse by specific user groups. To address these challenges, we propose architectural solutions aimed at preventing inappropriate use and promoting responsible AI interactions.Comment: Extended version of Conversation 2023 position pape

Learning in the presence of partial observability and concept drifts

This thesis is divided in two parts where we analyse machine learning algorithms in two different contexts. In the first part we study reinforcement learning algorithms, specially policy gradient methods for partially observable Markov decision processes (POMDPs). We use a special class of policy structure represented by a finite state controller. A comparison of different policy based methods are made and the performance of these methods are compared with the existing planning solutions for different POMDP environments used in the literature. The second part of this thesis outlines the problem of concept drifts for time series data where we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We highlight that concept drifts play a major role in deteriorating the predictive accuracy of models trained for room level localization with WiFi signals and propose a phase and magnitude augmented feature space that is resistant to drifts. We study different learning algorithms with this new feature space and compare their performance in the presence of drifts.Cette thèse est divisée en deux parties où nous analysons des algorithmes d'apprentissage automatique dans deux contextes différents. Dans la première partie, nous étudions les algorithmes d'apprentissage par renforcement, en particulier les méthodes de gradient de politique pour les processus de décision de Markov partiellement observables (POMDP). Nous utilisons une classe spéciale de structure de règles représentée par un contrôleur d'états finis. Une comparaison de différentes méthodes basées sur des règles est effectuée et les performances de ces méthodes sont comparées aux solutions de planification existantes pour différents environnements POMDP utilisés dans la littérature. La deuxième partie de cette thèse décrit le problème des dérives conceptuelles pour les données de séries chronologiques, où nous analysons l'incohérence temporelle de la transmission en continu de signaux sans fil dans le contexte de la localisation intérieure passive sans appareil. Nous soulignons que les dérives conceptuelles jouent un rôle majeur dans la détérioration de la précision prédictive des modèles formés pour la localisation au niveau de la pièce avec des signaux WiFi et proposons un espace de fonctions à augmentation de phase et de magnitude résistant aux dérives. Nous étudions différents algorithmes d'apprentissage avec ce nouvel espace de fonctions et comparons leurs performances en présence de dérives

Mobile VoIP user experience in LTE

3GPP Long-term Evolution (LTE) systems being deployed are fast gaining market shares. High data rates (approaching 100 Mbit/s in the downlink direction and 50 Mbit/s for uplink connections) and small delays are attractive features of LTE. Spectrum flexibility also makes deployment easy on various frequency bands in different parts of the world. However, as LTE offers packet switched services only, mobile broadband connectivity has become the dominant LTE application so far. This paper studies user-perceived quality of service for a mobile Voice over IP (VoIP) application in LTE. Results were achieved using the OPNET Modeler simulation environment.Validerad; 2011; 20110704 (karand)NIMO - Nordic Interaction and Mobility Research Platfor

Indoor taxi-cab : real-time Indoor positioning and location-based services with Ekahau and Android OS

Positioning and routing in an outdoors environment is still challenging especially in complex buildings, where a number of buildings are combined with tunnels and bridges, and the GPS signal is unreachable. Wasting time for looking for a particular room in an unfamiliar huge indoor environment or a product in an enormous store is a real life problem that everybody faces on daily basis. The paper represents a solution for addressed problem by using Ekahau positioning systems and Android OS through an intermediary server, which acts between these two systems to provide actual room level position on a map by mathematical modeling technique. The system also based on request provides shortest path for a certain destination by computing Dijkstra's search algorithm. The distance between the locations is defined based on Taxi-cab geometry distance definition for the mobile clients. Additionally, the users can also display shortest path for some nearest items such as coffee machine. The implemented system evaluations are carried out in a basement floor on the site.Godkänd; 2014; 20140307 (karand)</p