Forecasting store foot traffic using facial recognition, time series and support vector machines

In this paper, we explore data collected in a pilot project that used a digital camera and facial recognition to detect foot traffic to a sports store. Using a time series approach, we model daily incoming store traffic under three classes (all faces, female, male) and compare six forecasting approaches, including Holt-Winters (HW), a Support Vector Machine (SVM) and a HW-SVM hybrid that includes other data features (e.g., weather conditions). Several experiments were held, under a robust rolling windows scheme that considers up to one week ahead predictions and two metrics (predictive error and estimated store benefit). Overall, competitive results were achieved by the SVM (all faces), HW (female) and HW-SVM (male) methods, which can potentially lead to valuable gains (e.g., enhancing store marketing or human resource management).This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013

A sequence to sequence long short-term memory network for footwear sales forecasting

Footwear sales forecasting is a critical task for supporting product managerial decisions, such as the management of footwear stocks and production levels. In this paper, we explore a recently proposed Sequence to Sequence (Seq2Seq) Long Short-Term Memory (LSTM) deep learning architecture for multi-step ahead footwear sales Time Series Forecasting (TSF). The analyzed Seq2Seq LSTM neural network is compared with two popular TSF methods, namely ARIMA and Prophet. Using real-world data from a Portuguese footwear company, several computational experiments were held. Focusing on daily sales, we analyze data recently collected during a 3-year period (2019-2021) and related with seven types of products (e.g., sandals). The evaluation assumed a robust and realistic rolling window scheme that considers 28 training and testing iterations, each related with one week of multi-step ahead predictions. Overall, competitive predictions were obtained by the proposed LSTM model, resulting in a weekly Normalized Mean Absolute Error (NMAE) that ranges from 5% to 11%.- This work was financed by the project "GreenShoes 4.0 Calcado, Marroquinaria e Tecnologias Avancadas de Materiais, Equipamentos e Software" (N. POCI-01-0247-FEDER-046082), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Implementation of artificial intelligence solutions in a trade company to attract customers – buyers of pharmaceutical products

В условиях современного конкурентного рынка торговые компании, особенно работающие в фармацевтическом секторе, сталкиваются с проблемой привлечения и удержания клиентов. Использование технологий искусственного интеллекта привлекло значительное внимание как средство повышения вовлеченности клиентов и улучшения результатов бизнеса. В этой диссертации исследуется актуальность внедрения решений искусственного интеллекта в торговой компании для решения этих проблем и извлечения выгоды из появляющихся возможностей. Целью магистерской диссертации является исследование потенциала и предложение стратегии внедрения решений искусственного интеллекта в деятельность торговой компании для привлечения клиентов, приобретающих фармацевтическую продукцию. Объект исследования – информационные технологии в торговой деятельности. Предметом исследования является стратегия эффективного внедрения ИИ-решений, направленная на привлечение клиентов фармацевтической продукции. Исследование направлено на понимание того, как ИИ может оптимизировать различные аспекты деятельности компании, чтобы повысить вовлеченность клиентов и улучшить общую эффективность бизнеса. В задачи исследования входит выявление наиболее эффективных приложений искусственного интеллекта, анализ их влияния на поведение и удовлетворенность клиентов, а также предложение стратегии и плана ее успешной реализации на практике. Эта диссертация дополняет существующий объем знаний, исследуя конкретное применение решений искусственного интеллекта в контексте привлечения клиентов, приобретающих фармацевтическую продукцию. В нем исследуются новые подходы и стратегии использования технологий искусственного интеллекта, таких как машинное обучение, обработка естественного языка и персонализированные рекомендации, для создания инновационного опыта работы с клиентами и получения конкурентного преимущества на рынке. Результаты этого исследования имеют практическое значение для торговых компаний, работающих в фармацевтическом секторе. Внедряя решения искусственного интеллекта, компании могут улучшить взаимодействие с клиентами, повысить точность прогнозирования спроса, оптимизировать управление запасами и предоставлять персонализированные рекомендации по продуктам. Эти результаты могут повысить удовлетворенность клиентов, увеличить продажи и обеспечить долгосрочную лояльность клиентов.In today's competitive marketplace, trade companies, particularly those operating in the pharmaceutical sector, face the challenge of attracting and retaining customers. The utilization of AI technologies has gained significant attention as a means to enhance customer engagement and improve business outcomes. This thesis explores the relevance of implementing AI solutions in a trade company to address these challenges and capitalize on emerging opportunities. The purpose of this master's thesis is to investigate the potential and to propose the strategy of AI solutions implementation in a trade company's operations to attract customers who purchase pharmaceutical products. The object of the study is the information technologies in the trading activity. The subject of the study is the strategy of effective implementation of AI solutions aimed at attracting customers of pharmaceutical products. The research focuses on understanding how AI can optimize various aspects of the company's operations to enhance customer engagement and improve overall business performance. The objectives of the study include identifying the most effective AI applications, analyzing their impact on customer behavior and satisfaction, and proposing a strategy and a plan for its successful implementation in practice. This thesis contributes to the existing body of knowledge by examining the specific application of AI solutions in the context of attracting customers who purchase pharmaceutical products. It explores novel approaches and strategies to leverage AI technologies, such as machine learning, natural language processing, and personalized recommendations, to create innovative customer experiences and gain a competitive edge in the market. The findings of this research have practical implications for trade companies operating in the pharmaceutical sector. By implementing AI solutions, companies can enhance customer engagement, improve the accuracy of demand forecasting, optimize inventory management, and provide personalized product recommendations. These outcomes have the potential to drive customer satisfaction, increase sales, and establish long-term customer loyalty

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Deep Multi Temporal Scale Networks for Human Motion Analysis

The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature

Disruptive Technologies with Applications in Airline & Marine and Defense Industries

Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine