This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis.

This work consists of a study to detect prostate cancer using E-senses devices based on electronic tongue and electronic nose systems. Therefore, two groups of confirmed prostate cancer and control patients were invited to participate through urine and exhaled breath samples, where the control patients group was categorized as Benign Prostatic Hyperplasia, Prostatitis, and Healthy patients. Afterward, the samples were subsequently classified using Pattern Recognition and machine learning methods, where the results were compared through clinical history, obtaining a 92.9% success rate in the PCa and control samples’ classification accuracy by using eTongue and a 100% success rate of classification using eNose.

This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using HS-SPME-GC-MS analysis. The best machine learning models from the different classification methods reached a 100% success rate in classifying the samples. The proposal of this study was to enhance the classification of Colombian herbal teas using three sensory perception systems. This was achieved by consolidating the data obtained from the collected samples.

The combination of an electronic nose and an electronic tongue represents a significant advance in the pursuit of effective detection methods for prostate cancer, a widespread form of cancer affecting men across the globe. These cutting-edge devices, collectively called “E-Senses”, use data fusion to identify distinct chemical compounds in exhaled breath and urine samples, potentially improving existing diagnostic techniques. This study combined the information from two sensory perception devices to detect prostate cancer in biological samples (breath and urine). To achieve this, data from patients diagnosed with the disease and from control individuals were collected using a gas sensor array and chemical electrodes. The signals were subjected to data preprocessing algorithms to prepare them for analysis. Following this, the datasets for each device were individually analyzed and subsequently merged to enhance the classification results. The data fusion was assessed and it successfully improved the accuracy of detecting prostate-related conditions and distinguishing healthy patients, achieving the highest success rate possible (100%) in classification through machine learning methods, outperforming the results obtained from individual electronic devices.

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission.