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Background: Capabilities of Artificial Intelligence (AI) are rapidly advancing, as are its potential applications. Examples of the adoption of AI in social work already exist, but an overview of its manifold uses is lacking. This review aimed to systematically assess the existing research focused on the uses of AI applications in social work practice and to spotlight use-cases yet to be explored. Methods: A scoping review was conducted guided by Arksey and O'Malley's framework and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis, extension for Scoping Review (PRISMA-ScR). A systematic search was performed using the Scopus database. Eligibility criteria included pre-prints and published articles from January 2000 to April 2023 that emphasized AI implementations in social work practice. No limitations were placed on study design. Data extracted included: article details; country of study; the AI use-case and task; and the specific AI technology employed. Extracted data from all eligible studies were collated using tables and accompanied by narrative descriptive summaries. The review employed CAIMeR (a theory explaining the results of social work interventions) to  pinpoint gaps and highlight novel unexplored applications of AI in social work.  Results: Of the 159 identified articles, 28 satisfied the inclusion criteria. On average, three relevant publications surfaced annually, with approximately 60% hailing from the US. Notably, the absolute majority of the applications of AI were concentrated on predicting or elucidating individual’s health or social condition. Conclusion: Although AI possesses substantial potential, current research into its applications in social work remains surprisingly sparse and averaging a mere three studies annually. The prevailing emphasis of this research is on discerning individual health or social conditions. Given AI's multifaceted capabilities, there exists a substantial opportunity to broaden research into other applications. Informed by the CAIMeR theory, this review identifies several unexplored applications of AI paving the way for future research. / Bakgrund: Utvecklingen inom Artificiell Intelligens (AI) medför betydande potentiella fördelar och utmaningar, vilket understryker behovet för det socialt arbetets praktik att anpassa och ta till sig dess användning. Denna studie undersöker användningen av AI inom socialt arbete genom att kartlägga inom vilka domäner av socialt arbete AI har använts och för vilket syfte. Därtill identifieras forskningsluckor och nya användningsområden för AI med hjälp av CAIMeR teorin. Metod: Genom att använda en scoping review metodik vägledd av Arksey och O'Malleys ramverk och PRISMA-ScR:s riktlinjer, utfördes en systematisk sökning i Scopus fram till april 2023 med fokus på artiklar som diskuterar AI:s implementering i socialt arbete. Resultat: Av 159 artiklar som hittades uppfyllde 28 inkluderingskriterierna. AI har använts flitigt inom socialt arbete, främst för att förutsäga eller diagnostisera individers tillstånd. Forskningsvolymen är begränsad, med ungefär tre studier som genomförts årligen. Slutsats: Trots AI:s potential att förbättra socialt arbete visar nuvarande litteratur en begränsad forskningsvolym om ämnet och ett begränsat användningssätt för AI. Nästan uteslutande koncentrerar sig studierna på användningen av AI för att förutsäga sociala problem eller hälsotillstånd. Studien identifierar ett behov av att utforska AI inom flera användningsområden inom socialt arbete. Med hjälp av CAIMeR-teorin presenterar denna studie flera sådana potentiella användningsområden av AI.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)