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We present ZipML, a new path-based type system for a fully fledged ML-module language that avoids the signature avoidance problem. This is achieved by introducing floating fields, which act as additional fields of a signature, invisible to the user but ...

Machine learning (ML) has been increasingly adopted in the field of cyber security to enhance the detection and prevention of cyber threats. However, recent studies have demonstrated that ML-based cyber security systems are vulnerable to adversarial ...

In the age of the internet of things (IoT), ensuring security is crucial to protect the interconnected devices and systems. The capacity to identify cyberattacks is essential for IoT security, hence many academics have focused their efforts on developing ...

Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers. They have been applied to various problems including Boolean Satisfiability, Traveling Salesperson and Graph Coloring. These ...

• First research on Algorithm Selection and Anytime Algorithm Selection for Pseudo Boolean Optimization (PBO).
• Inclusion of a “no solution” special label to predict when a solution is not expected for a given time limit.
• Inclusion of ...

The Healthcare Internet of Things (H-IoT) is a rapidly developing problem solving model with significant potential to improve patient care and healthcare outcomes. This study focuses on integrating cryptographic platforms into H-IoT systems to ...

RS are becoming more crucial in everyone’s life as the need for recommendations that reflect users’ interests grows every day. For new users, the movie RS based on a film’s ratings has become an exciting trait. However, scalability, cold start, ...

The main objective of this systematic literature review is to unveil the prevailing trend of employing cutting-edge models for bankruptcy prediction for a period spanning from 2012 to mid-2023. Employing the PRISMA method, we reviewed 207 ...

Heart disease poses a serious threat, and the occurrence of a heart attack can lead to premature and fatal situations. Recent investigations in data mining techniques have delivered valuable insights into analyzing cardiac data through sensing, ...

Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance ...

Industrial control systems (ICS) are vital parts of the physical infrastructure for many industrial assets, such as oil and gas fields, water stations, and power generation plants. Inadequate protection of such critical assets may lead to ...

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• Mechanically harvesting strawberries is challenging due to the labor intensity and susceptibility of the fruit to mechanical damage.
• A comprehensive review of recent progress in mechanical harvesting of fresh market strawberries was ...

This article reviews the research and development progress of mechanical harvesting technologies for fresh market strawberries in the past few decades, focusing on the main technologies such as cut-and-separate method, harvest-assist method, ...

Flaky tests are tests that pass and fail on different executions of the same version of a program under test. They waste valuable developer time by making developers investigate false alerts (flaky test failures). To deal with this issue, many prediction ...

Problem-solving strategies have been investigated in various informatics education contexts. However, no substantial research has yet been conducted on the problem-solving behavior of students in the field of Machine Learning (ML). This study aims ...$\mathrm{}$

The control of indoor temperature in greenhouses is crucial as it directly impacts the crop's thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can ...

This paper investigates the specific properties of Explainable Artificial Intelligence (xAI), particularly when implemented in AI/ML models across high-stakes sectors, in this case cybersecurity. The authors execute a comprehensive systematic ...

• Bridging xAI theory and practice.
• Systematic review of xAI metrics and frameworks.
• Experimental evaluation of various xAI explanations.
• The results show many metrics are ine2ective.
• The abundance of metrics has pros and ...

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to its effectiveness, simplicity, and generalization performance (i.e., test ...

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular ...

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• Feature Selection from Multi-Omics Data: Employed a feature-selection process integrating multi-omics data from TCGA.
• Machine Learning Integration: Utilized a range of machine learning classifiers to classify LUAD subtypes.
• ...

• Maintaining appropriate water quality is essential for aquaponics productivity and sustainability.
• Factors affecting aquaponics’ water quality and different approaches to control them are discussed and the importance of careful ...

The fast growth of the world population associated with the ever-increasing need for food and the significant contribution of agriculture to anthropogenic global warming is driving the changes from conventional farming approaches to innovative ...

This paper presents a novel Deep Learning (DL) framework for cotton plant disease prediction based on Explainable Artificial Intelligence (XAI) and Copula entropy based-Grey Wolf Optimization (GWO) algorithm. The suggested framework uses a cotton ...

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• Hybrid DL-GWO model is proposed to detect cotton diseases.
• Copula Entropy is proposed to enhance the GWO initialization.
• XAI is used as a second layer for explaining and enhancing the feature importance.
• The proposed model ...

Data-driven models can reduce the number of hardware sensors in a process plant by acting as low-cost substitutes for hardware sensors. Since some data-driven models have difficulty dealing with nonlinear data, kernel functions have been ...

• Progress and gaps in kernel functions and data-driven models are presented.
• Kernel functions for classification and regression are critically reviewed.
• Integrating kernel functions with adaptive data-driven models is recommended.