Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability by Sangwon Byun, Ah Young Kim, Min-Sup Shin, Hong Jin Jeon, Hong Jin Jeon, Chul-Hyun Cho, Chul-Hyun Cho

“…This study evaluated the feasibility of using machine-learning algorithms to detect stress automatically in MDD and PD patients, as well as healthy controls (HCs), based on HRV features.MethodsThe study included 147 participants (MDD: 41, PD: 47, HC: 59) who visited the laboratory up to five times over 12 weeks. …”
Get full text

Prediction Models for Diabetes in Children and Adolescents: A Review by Livija Cveticanin, Marko Arsenovic

“…It highlights models for predicting both type 1 and type 2 diabetes in this population, emphasizing the inclusion of risk factors that facilitate the identification of potential occurrence and early detection of diabetes in young individuals. Newly identified factors for differentiating between types of diabetes are discussed, alongside an overview of various machine learning and deep learning algorithms specifically adapted for diabetes prediction in children and adolescents. …”
Get full text

Object Recognition and Positioning with Neural Networks: Single Ultrasonic Sensor Scanning Approach by Ahmet Karagoz, Gokhan Dindis

“…With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images.…”
Get full text

Role of artificial intelligence in healthcare insurance: systematic literature review by Ahmed Ali Alkhelb, Salah Alshagrawi

Get full text

Real-time monitoring to predict depressive symptoms: study protocol by Yu-Rim Lee, Jong-Sun Lee

“…A machine learning algorithm and multilevel model will be used to construct a predictive model for depressive symptoms using the collected data.DiscussionThis study explores the potential of wearable devices and smartphones to improve the understanding and treatment of depression in young adults. …”
Get full text

An Efficient Optimal CapsNet Model-Based Computer-Aided Diagnosis for Gastrointestinal Cancer Classification by Fahdah A Almarshad, Prasanalakshmi Balaji, Liyakathunisa Syed, Eman Aljohani, Santhi Muttipoll Dharmarajlu, Thavavel Vaiyapuri, Nourah Ali AlAseem

“…Gastrointestinal or gastric cancer (GC) classification is a serious field of medical research and healthcare technology, where innovative machine learning (ML) and deep learning (DL) models are employed to categorize and analyze many kinds of GCs like pancreatic, gastric, or colorectal cancer. …”
Get full text

Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…First, we observed that automatic heart-sound segmentation algorithms—commonly used for data augmentation—produce varying outcomes, raising concerns about the accuracy of both the segmentation process and the resulting classification performance. …”
Get full text

Precision neuropsychology in the area of AI by Astri J. Lundervold

“…Key opportunities include enhanced pattern recognition in traditional assessments (e.g., digital clock drawing), continuous monitoring of symptom fluctuations (e.g., Attention Deficit Disorder), and personalized assessment and treatment procedures based on individual needs (e.g., learning disorders). The paper also addresses critical implementation challenges: ethical considerations including algorithmic bias and data privacy; balancing quantitative AI analytics with qualitative clinical expertise to avoid reductionism; and developing new competencies for neuropsychologists to effectively integrate AI in their research and clinical work. …”
Get full text

Artificial Intelligence in Breast Reconstruction: A Narrative Review by Andrei Iulian Rugină, Andreea Ungureanu, Carmen Giuglea, Silviu Adrian Marinescu

Get full text

A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics by Arash Shahbazpoor Shahbazi, Farzin Irandoost, Reza Mahdavian, Seyedehsamaneh Shojaeilangari, Abdollah Allahvardi, Hossein Naderi-Manesh

“…To quantitatively evaluate our algorithm, extensive experiments were conducted using available annotated data, confirming the reliability and robustness of our method. …”
Get full text

Crowd Evacuation in Stadiums Using Fire Alarm Prediction by Afnan A. Alazbah, Osama Rabie, Abdullah Al-Barakati

“…This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. …”
Get full text

Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration by Francesco Saverio Sorrentino, Marco Zeppieri, Carola Culiersi, Antonio Florido, Katia De Nadai, Ginevra Giovanna Adamo, Marco Pellegrini, Francesco Nasini, Chiara Vivarelli, Marco Mura, Francesco Parmeggiani

Get full text

Integrated Platform for Bio-Image Processing and Identification of Cervical Cancer by Larisa-Dalina LAZĂR, Diogen BABUC, Todor IVAŞCU

“…Considering convex models and papers, CNN showed the best results on augmented cervical cells data. In comparison, traditional machine learning algorithms, concretely K-Nearest Neighbors and Naive Bayes, produced lower results, emphasizing the advantages of deep learning models in medical image analysis. …”
Get full text

Multi-camera video collaborative analysis method based on edge computing by Zhibo QI, Lei DU, Ru HUO, Fan YANG, Tao HUANG

“…In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.…”
Get full text

Direct Estimation of Forest Aboveground Biomass from UAV LiDAR and RGB Observations in Forest Stands with Various Tree Densities by Kangyu So, Jenny Chau, Sean Rudd, Derek T. Robinson, Jiaxin Chen, Dominic Cyr, Alemu Gonsamo

“…Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. …”
Get full text

A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions by Ioannis Syllaidopoulos, Klimis S. Ntalianis, Ioannis Salmon

“…However, critical concerns such as algorithmic bias, data quality limitations, and governance challenges introduce significant obstacles to their deployment. …”
Get full text

Association between pro-inflammatory diet and fecal incontinence: a large population-based study by Haiyang Wang, Haiyang Wang, Zihan Liu, Zihan Liu, Xingfu Lu, Enyu Luan, Enyu Luan, Tingting Dong, Tingting Dong, Can Li, Can Li, Yanni Lv, Erkang Wu, Tao Shen, Tao Shen

Get full text

Joined Spatial and Spectral Segmentation of Hyperspectral Datasets on Historical Art Objects by Lingxi Liu, Aurore Malmert, Emeline Pouyet, Silvia Mirri, Giovanni Delnevo

“…In the context of clustering and classification, the choice between spatial and spectral features hinges on data characteristics and analytical goals. Spatial features excel in spatially contextualised data like urban planning and object detection, capturing adjacent pixel relationships. …”
Get full text

Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India by Ramachandran Rajalakshmi, Thyparambil Aravindakshan PramodKumar, Ashis Kumar Dhara, Geetha Kumar, Naziya Gulnaaz, Shramana Dey, Sourav Basak, B Uma Shankar, Raka Goswami, Raja Mohammed, Suchetha Manikandan, Sushmita Mitra, Harsimran Thethi, Saravanan Jebarani, Sinnakaruppan Mathavan, Tamilselvi Sarveswaran, Ranjit Mohan Anjana, Viswanathan Mohan, Sambuddha Ghosh, Tushar Kanti Bera, Rajiv Raman

“…The AI tools primarily utilize deep learning (DL) techniques and are tailored to analyse extensive medical image data and provide diagnostic outputs, essentially streamline the DR screening process. …”
Get full text

Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate by Hussein A. Saleem, Abebe Temesgen Ayalew

“…Automated environmental monitoring systems, incorporating IoT sensors and machine-learning algorithms, provide real-time data on air quality, dust levels, and noise pollution. …”
Get full text