Introduces an AI framework for deep learning applications in grinding processes.

Child obesity is an increasingly pervasive problem. Traditional therapy programs are time- and cost-intensive and their success is often not guaranteed due to the individual characteristics of patients. Thus, a more patient-centric approach is necessary. Furthermore, rural populations in low-income areas often suffer from difficult access to healthcare. In this position paper, we introduce a data-driven system which uses low-cost devices for predicting performance and therapy success of obese children by applying machine learning methods. By using data-driven systems for e.g. predicting outcomes of a therapy, physicians could personalize standard therapies and improve the outcome bringing low-income areas within reach for quality healthcare. The envisioned data-driven system as an output from our mHealth project provides positive evidence as a tool for personalized mHealth systems among physicians.

One of the main barrier for electric vehicles to be successful in real world is the need for expensive charging infrastructures. The key aspect of EV is time required to charge the battery to full capacity is far less than the time duration for which the car remains available for charging. Smart charging system can leverage this aspect to efficiently manage the load demand, which in turn alleviates the need for more than necessary number of expensive charging infrastructures. EV user behaviour prediction is vital for building EV Adaptive Charging System. In the past there have been several statistical and ML methods that tries to predict EV user behavior. But with the influx of huge amount of EV user data and deep learning's (DL) ability to perform well on such large data enables us to build DL based methods to predict EV user behavior. In this paper, we predict EV user behavior using ML and DL methods and compare the results and infer the insights for difference in performance. By comparing at various settings between machine learning (ML) and DL methods, we found that K-Nearest Neighbours outperforms Neural Networks with a very minute difference of 0.031 in Mean Absolute Error metric.

Introduces a framework for learning user preferences to select high-quality 3D designs.

A methodology for assessing the perceptual quality of image processing using combined contrast distributions.

Explores real-time optimization techniques for agricultural production using autonomous systems.

Discusses the role of sensing technologies and wearables in advancing research methodologies.

In the age of mobile cloud computing web-based systems are often designed to transfer data to large scaling online storage facilities in order to persistently save and analyze it with complex algorithms such as used in machine learning. These systems often require a reliable network connection, which does not hold for a variety of mobile business applications. As an alternative to traditional cloud-based systems the TUCANA approach makes use of the local processing power of mobile edge devices in order to come up with high complex AI pipelines processing data in real-time. By applying the idea of TUCANA to our service use-case called “nPotato” we developed an artificial, nociceptive potato that frequently measures and analyses acceleration data during the harvesting process of potatoes. In the given scenario sensory data is processed locally in real-time using the device’s local computing power to gain higher productivity in the area of precision farming.

Presents a system for predicting therapy success for obese children using data analytics.

Discusses the intersection of data-driven and theory-driven research in information systems.

Discusses the evolving nature of work and enterprise systems in the context of technological advancements.

Explores the use of machine learning for identifying structural models through inductive discovery.

Explores innovation management strategies tailored for start-ups.

Proposes a joint approach for input and model parameter selection in financial forecasting.

Highlights the role of smart services in agriculture, using the digitization of potatoes as a case study.