A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Sleep-dependent consolidation in multiple memory systems

Before newly formed memories can last for the long-term, they must undergo a period of consolidation. It has been shown that sleep facilitates this process. One hypothesis about how this may occur is that learning-related neuronal activity is replayed during following sleep periods. Such a reactivation of neural activity patterns has been repeatedly shown in the hippocampal formation in animals. Hippocampally-induced reactivation can also be observed in other brain areas like the neocortex and basal ganglia. On the behavioral level, sleep has been found to benefit performance on a broad range of memory tasks that rely on different neural systems. Up to now, however, it is unclear whether the same mechanisms mediate effects of sleep on consolidation in different memory systems. In this thesis, we investigated both the effects and the mechanisms of sleep-dependent consolidation in multiple memory systems. We find that sleep benefits performance on a broad range of procedural and declarative memory tasks (studies 1 and 2). These beneficial effects of sleep go beyond a reduction of retroactive interference as effected by quiet wakeful meditation (study 1). In study 2, we demonstrate that the processes underlying these beneficial effects of sleep are different for different memory systems. We assessed performance on typical declarative and procedural memory tasks during one week after participants slept or were sleep deprived for one night after learning. Sleep-dependent consolidation of hippocampal and non-hippocampal memory follows different time-courses. Hippocampal memory shows a benefit of sleep only one day after learning. Performance after sleep deprivation recovers following the next night of sleep, so that no enduring effect of sleep can be observed. However, sleep deprivation before recall does not impair performance. For non-hippocampal memory, on the other hand, long-term benefits of sleep after learning can be observed even after four days. Here, delayed sleep cannot rescue performance. This indicates a dissociation between two sleep-related consolidation mechanisms, which rely on distinct neuronal processes. We studied the neuronal processes underlying sleep effects on declarative memory in study 3, where we investigate learning-related electrophysiological activity in the sleeping brain. With the help of multivariate pattern classification algorithms, we show that brain activity during sleep contains information about the kind of visual stimuli that were learned earlier. We thus find that learned material is actively reprocessed during sleep. In a next step, we examined whether procedural memory can also benefit from reactivation during sleep. We find that a procedural memory task that has been found to activate the hippocampus can be strengthened by externally cueing the reactivation process during sleep. Similar to study 2, this indicates that it is not the traditional distinction between declarative and procedural memory that determines how memories are consolidated during sleep. Rather, memory systems, and in particular hippocampal contribution, decide the sleep-dependent consolidation process. In the first four studies, we examined how sleep affects memory in different memory systems. In our last study, we went one step further and investigated whether multiple memory systems can also interact during consolidation in sleep. We devised a task during which both implicit and explicit memory develop during learning. Results show that sleep not only strengthens implicit and explicit memory individually, it also integrates these formerly separate representations of the learning task. Implicit and explicit memory are negatively correlated immediately after training. Sleep renders this association positive and allows cooperation between the two memory traces. We observe this change both in behavior, using structural equation modeling, and on the level of brain activity, measured by fMRI. After sleep, the hippocampus is more strongly activated during recall of implicit memory, whereas the caudate nucleus shows stronger activity during explicit memory recall. Moreover, both regions show correlated stimulus-induced responses in a task that allows memory systems cooperation. These results provide conclusive evidence that sleep not only strengthens memory, but also reorganizes the contributing neural circuits. In this way, sleep actually changes the quality of the memory representation

Converging Human Intelligence With AI Systems to Advance Flood Evacuation Decision Making

The powers that artificial intelligence (AI) has developed are astounding, with recent success in integrating into a human cognitive workflow. AI will attain its full potential only if, as part of its intelligence, it also actively teams up with humans to co-create solutions. Combining AI simulation with human understanding and strategic abilities through data convergence may optimize the process and provide a capacity akin to teaming intelligence. This thesis will introduce the concepts of Human AI Convergence (HAC) capabilities for flood evacuation decision-making. The concept introduced in this thesis is the first step toward the HAC concept in weather disaster applications. This research demonstrates a synergy between humans and AI by integrating the data produced by humans through social media with an AI system to enhance a flood evacuation decision-making problem. The prediction from Long short-term memory (LSTM) and a river hydraulic model, i.e., Height Above Nearest Drainage (HAND), is integrated with human data from X (previously Twitter) to visualize flood inundation areas, which acts as a 3rd party agent for a HAC system. The goal is to synthesize and analyze HAC competence in flood evacuation emergency management and harness the full potential of AI as a partner in real-time planning and decision-making. This thesis has explored why HAC intelligence is essential to emergency planning and decision-making, providing a general structure for researchers to use HAC to devise effective systems that cooperate well and evaluate state-of-the-art, and, in doing so, providing a research agenda and a roadmap for future flood evacuation emergency management, rescue, and decision making. This state-of-the-art flood evacuation product stands to advance the frontier of human-AI collaborative research significantly

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Scene Buildup From Latent Memory Representations Across Eye Movements

An unresolved problem in eye movement research is how a representation is constructed on-line from several consecutive fixations of a scene. Such a scene representation is generally understood to be sparse; yet, for meeting behavioral goals a certain level of detail is needed. We propose that this is achieved through the buildup of latent representations acquired at fixation. Latent representations are retained in an activity-silent manner, require minimal energy expenditure for their maintenance, and thus allow a larger storage capacity than traditional, activation based, visual working memory. The latent representations accumulate and interact in working memory to form to the scene representation. The result is rich in detail while sparse in the sense that it is restricted to the task-relevant aspects of the scene sampled through fixations. Relevant information can quickly and flexibly be retrieved by dynamical attentional prioritization. Latent representations are observable as transient functional connectivity patterns, which emerge due to short-term changes in synaptic weights. We discuss how observing latent representations could benefit from recent methodological developments in EEG-eye movement co-registration