Editor's Note

The International Journal of Interactive Multimedia and Artificial Intelligence – IJIMAI (ISSN 1989-1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances in Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. The present volume (June 2022), consists of 20 articles of diverse applications of great impact in several fields. The issue consistently showcases the utilization of AI techniques or mathematical models with an artificial intelligence base, as a standard element. Different manuscripts on usability and satisfaction, machine learning models, genetic algorithms, computer entertainment technologies, oral pathologies, optimistic motion planning, data analysis for decision making, etc. can be found in this volume

A Session based Multiple Image Hiding Technique using DWT and DCT

This work proposes Steganographic technique for hiding multiple images in a color image based on DWT and DCT. The cover image is decomposed into three separate color planes namely R, G and B. Individual planes are decomposed into subbands using DWT. DCT is applied in HH component of each plane. Secret images are dispersed among the selected DCT coefficients using a pseudo random sequence and a Session key. Secret images are extracted using the session key and the size of the images from the planer decomposed stego image. In this approach the stego image generated is of acceptable level of imperceptibility and distortion compared to the cover image and the overall security is high.Comment: 4 pages,16 figures, "Published with International Journal of Computer Applications (IJCA)

Decision support system for spare parts warehousing

Spare parts warehousing decision-making plays an important role in today's manufacturing industry as it derives an optimum inventory policy for the organizations. Previous research on spare parts warehousing decision-making did not deal with the problem holistically considering all the subjective and objective criteria of operational and strategic needs of the manufacturing companies in the process industry. This study reviews current relevant literature and develops a conceptual framework (an integrated group decision support system) for selecting the most effective warehousing option for the process industry using the analytic hierarchy process (AHP). The framework has been applied to a multinational cement manufacturing company in the UK. Three site visits, eight formal interviews, and several discussions have been undertaken with personnel of the organization, many of which have more than 20 years of experience, in order to apply the proposed decision support system (DSS). Subsequently, the DSS has been validated through a questionnaire survey in order to establish its usefulness, effectiveness for warehousing decision-making, and the possibility of adoption. The proposed DSS is an integrated framework for selecting the best warehousing option for business excellence in any manufacturing organization

Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors