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To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors.

Sensory science provides objective information about the consumer understanding of a product, the acceptance or rejection of stimuli, and the description of the emotions evoked. It is possible to answer how consumers perceive a product through discriminative and descriptive techniques. However, perception can change over time, and these fluctuations can be measured with time-intensity methods. Instrumental sensory devices and immersive techniques are gaining headway as sensory profiling techniques. The authors of this paper critically review sensory techniques from classical descriptive analysis to the emergence of novel profiling methods. Though research has been done in the creation of new sensory methods and comparison of those methods, little attention has been given to the timeline approach and its advantages and challenges. This study aimed to gather, explain, simplify, and discuss the evolution of sensory techniques.

Object detection is a technique of computer vision whose primary intent is to detect objects. The objects can be detected from any image or video feeds. Now a day’s object detection is extensively applied in video surveillance systems, human tracking, and self-driving cars. This paper presented a novel object detection approach that uses only wireframe-based features. The wireframe of the image is identified by using Cellular logical array processing. This technique can determine the visual and geometric features of the image . This paper focuses on a deep neural network framework to detect the target object in the image. Fast R-CNN is used for the detection of objects. The detection speed is fast because only the wireframe of the image is obtained first and then fed into the Fast RCNN model for detection and classification purposes. The performance of the proposed methodology is evaluated on PASCAL VOC, example-based synthesis dataset and real-time dataset. The proposed methodology gives mean average precision (mAP) 89.4%, 91.33% and 88.1% on PASCAL VOC, example-based and real-time dataset. The experimental analysis demonstrated that our proposed detection method achieves better results than the other state of art methods. The approach is helpful to detect the 2D and 3D objects as well.

When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars. The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis. To enhance accuracy and comprehension in complex situations, DFN makes use of a combination of deep learning and fusion techniques. With a minimum gain of 6.4% in accuracy for the SUN-RGB-D dataset and 3.6% for the NYU-Dv2 dataset. Findings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized.

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

It is well established that treatment regime compliance is linked to the acceptability of a pharmaceutical formulation, and hence also to therapeutic outcomes. To that end, acceptability must be assessed during the development of all pharmaceutical products and especially for those intended for paediatric patients. Although acceptability is a multifaceted concept, poor sensory characteristics often contribute to poor patient acceptability. In particular, poor taste is often cited as a major reason for many patients, especially children, to refuse to take their medicine. It is thus important to understand and, as far as possible, optimise the sensory characteristics and, in particular, the taste/flavour/mouthfeel of the formulation throughout the development of the product. Sensory analysis has been widely practiced, providing objective data concerning the sensory aspects of food and cosmetic products. In this paper, we present proposals concerning how the well-established principles of sensory analysis can best be applied to pharmaceutical product development, allowing objective, scientifically valid, sensory data to be obtained safely. We briefly discuss methodologies that may be helpful in reducing the number of samples that may need to be assessed by human volunteers. However, it is only possible to be sure whether or not the sensory characteristics of a pharmaceutical product are non-aversive to potential users by undertaking sensory assessments in human volunteers. Testing is also required during formulation assessment and to ensure that the sensory characteristics remain acceptable throughout the product shelf life. We provide a risk assessment procedure to aid developers to define where studies are low risk, the results of a survey of European regulators on their views concerning such studies, and detailed guidance concerning the types of sensory studies that can be undertaken at each phase of product development, along with guidance about the practicalities of performing such sensory studies. We hope that this guidance will also lead to the development of internationally agreed standards between industry and regulators concerning how these aspects should be measured and assessed throughout the development process and when writing and evaluating regulatory submissions. Finally, we hope that the guidance herein will help formulators as they seek to develop better medicines for all patients and, in particular, paediatric patients.

In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Building extraction is very essential in various urban dynamics like disaster management and change detection, finding the estimated population, and so on. Building extraction from satellite data is a challenging task as the images may be subjected to different illumination or structure due to very large variations of the appearance of buildings which may correspond to the different area/terrain. Although satellite imagery is readily available from various sources, translating the imagery includes intensive effort. Many computer-vision tasks have been carried out successfully but understanding the impact of them on building extraction with remote sensing imagery is a growing need.To overcome this kind of problem, an algorithm is proposed which extends the convolutional neural network for pixel-wise classification of images. Furthermore, to resolve the problem of extraction and masking of images, Mask-RCNN (i.e., Mask Region-based Convolutional Neural Network) algorithm is used which makes this process easier and more efficient.The model is trained on a complex dataset that is significantly larger. Also, to make this algorithm more scalable, an advanced image augmentation technique is used in the pre-processing step.The results show that the algorithm achieves better performance in terms of accuracy.