Evaluation of Deep Neural Network and alternating decision tree for kiwifruit detection

Robotic kiwifruit harvesting systems are currently being introduced to improve the reliability and farming yields of kiwifruit harvesting operations. Machine learning is widely used to carry out the visual detection tasks required of such systems. This paper specifically compares two types of machine learning algorithms: the multivariate alternating decision tree and deep learning based kiwifruit classifiers. The purpose of the study is to investigate the cost of implementation against the classification performance. Thus, discussion is centred around computational cost and its impacts on the overall system architecture. We found that the traditional decision tree classifiers can achieve comparable classification performance at a fraction of the cost and complexity, providing robust and cost-effective instrument design

Maximal Associated Regression: A nonlinear extension to Least Angle Regression

This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates. For each predictor, MAR selects between a linear or additive fit as determined by the dataset. The proposed algorithm is an adaptation of Least Angle Regression (LARS) and retains its efficiency in building sparse models. Constrained penalized splines are used to generate smooth nonlinear transformations for the additive fits. A monotonically constrained extension of MAR (MARm) is also introduced in this paper to fit isotonic regression problems. The proposed algorithms are validated on both synthetic and real datasets. The performances of MAR and MARm are compared against LARS, Generalized Linear Models (GLM), and Generalized Additive Models (GAM) under the Gaussian assumption with a unity link function. Results indicate that MAR-type algorithms achieve a superior subset selection accuracy, generating sparser models that generalize well to new data. MAR is also able to generate models for sample deficient datasets. Thus, MAR is proposed as a valuable tool for subset selection and data exploration, especially when a priori knowledge of the dataset is unavailable

Enkephalon - technological platform to support the diagnosis of alzheimer’s disease through the analysis of resonance images using data mining techniques

Dementia can be considered as a decrease in the cognitive function of the person. The main diseases that appear are Alzheimer and vascular dementia. Today, 47 million people live with dementia around the world. The estimated total cost of dementia worldwide is US $ 818 billion, and it will become a trilliondollar disease by 2019 The vast majority of people with dementia not received a diagnosis, so they are unable to access care and treatment. In Colombia, two out of every five people presented a mental disorder at some point in their lives and 90% of these have not accessed a health service. Here it´s proposed a technological platform so early detection of Alzheimer. This tool complements and validates the diagnosis made by the health professional, based on the application of Machine Learning techniques for the analysis of a dataset, constructed from magnetic resonance imaging, neuropsychological test and the result of a radiological test. A comparative analysis of quality metrics was made, evaluating the performance of different classifier methods: Random subspace, Decorate, BFTree, LMT, Ordinal class classifier, ADTree and Random forest. This allowed us to identify the technique with the highest prediction rate, that was implemented in ENKEPHALON platform

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delay

Detecting systematic defects on semiconductor wafers through statistical analysis and data mining on production test data: development, implementation and application of automatic defect cluster analysis system

Semiconductor manufacturing test has traditionally been seen as a simple task that segregates good Devices-Under-Test (DUTs) from the defective ones (entirely or partially failed). Since recent years, this paradigm has been rapidly changing. As integrated circuit technology advances towards nano-scale geometry, high defect and fault rates are experienced throughout the semiconductor production process. The semiconductor industry has approached an inflection point whereby test-enabled diagnostics and yield learning have become crucial for further progress in Integrated Circuit (IC) manufacturing. The latest International Technology Roadmap for Semiconductors (ITRS) report on Test and Test Equipment has identified Detecting Systematic Defects as one of the industry’s most difficult challenges in the modern test technology area. Production test data was identified as an essential element to overcome these challenges in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying such cluster patterns is a crucial step towards improving the fabrication process and implementing real-time statistical process control. To address the industry’s needs, this research proposes an automatic defect cluster identification and recognition method. Its practical implementation utilises statistical analysis and data mining to discover defect cluster patterns from production test data. Statistical analysis is performed to distinguish between systematic and random failure patterns while a novel Segmentation, Detection and Cluster Extraction (SDC) algorithm is proposed to extract these defect clusters. The research proposes a novel complex number Alternating Decision Tree (ADTree) that incorporates features extracted using Rotational Moment Invariants with customised geometrical dimensions for cluster recognition. Experimental results show that the proposed approach, when implemented as a complete Automatic Defect Cluster Analysis System (ADCAS), offers the required high cluster detection and classification accuracy that is expected from the industry. The application can be done either on-line or off-line. The on-line industrial application is characterised by a short computational time and is well suitable for crucial areas such as defect-oriented testing, real-time statistical process control and fast fault diagnosis. The off-line applications are useful in a periodic manufacturing process performance review or for a general post-analysis of the manufacturing process. The outcome of this research has been presented in several journal and conference publications and transferred to the industry for implementation is mass-manufacturing of modern semiconductor products

Detecting systematic defects on semiconductor wafers through statistical analysis and data mining on production test data: development, implementation and application of automatic defect cluster analysis system

Semiconductor manufacturing test has traditionally been seen as a simple task that segregates good Devices-Under-Test (DUTs) from the defective ones (entirely or partially failed). Since recent years, this paradigm has been rapidly changing. As integrated circuit technology advances towards nano-scale geometry, high defect and fault rates are experienced throughout the semiconductor production process. The semiconductor industry has approached an inflection point whereby test-enabled diagnostics and yield learning have become crucial for further progress in Integrated Circuit (IC) manufacturing. The latest International Technology Roadmap for Semiconductors (ITRS) report on Test and Test Equipment has identified Detecting Systematic Defects as one of the industry’s most difficult challenges in the modern test technology area. Production test data was identified as an essential element to overcome these challenges in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying such cluster patterns is a crucial step towards improving the fabrication process and implementing real-time statistical process control. To address the industry’s needs, this research proposes an automatic defect cluster identification and recognition method. Its practical implementation utilises statistical analysis and data mining to discover defect cluster patterns from production test data. Statistical analysis is performed to distinguish between systematic and random failure patterns while a novel Segmentation, Detection and Cluster Extraction (SDC) algorithm is proposed to extract these defect clusters. The research proposes a novel complex number Alternating Decision Tree (ADTree) that incorporates features extracted using Rotational Moment Invariants with customised geometrical dimensions for cluster recognition. Experimental results show that the proposed approach, when implemented as a complete Automatic Defect Cluster Analysis System (ADCAS), offers the required high cluster detection and classification accuracy that is expected from the industry. The application can be done either on-line or off-line. The on-line industrial application is characterised by a short computational time and is well suitable for crucial areas such as defect-oriented testing, real-time statistical process control and fast fault diagnosis. The off-line applications are useful in a periodic manufacturing process performance review or for a general post-analysis of the manufacturing process. The outcome of this research has been presented in several journal and conference publications and transferred to the industry for implementation is mass-manufacturing of modern semiconductor products

Real-time Malaysian sign language translation using colour segmentation and neural network

In this paper we present an automatic visual-based sign language translation system. Our proposed automatic sign-language translator provides a real-time English translation of the Malaysia SL. To date, there have been studies on sign language recognition based on visual approach (video camera). However, the emphasis on these works is limited to a small lexicon of sign language or solely focuses on fingerspelling, which takes diferent approaches respectively. In practical sense, fingerspelling is used if a word cannot be expressed via sign language. Our sign language translator can recognise both fingerspelling and sign gestures that involve static and motion signs. Trained neural networks are used to identify the signs to translate into English