Shipping is integral to global trade, with increasing maritime traffic elevating safety risks. Accurate trajectory prediction based on Automatic Identification System (AIS) data is essential to prevent collisions and enhance navigational safety. This study introduces a novel Mamdani Fuzzy Inference System (MFIS)-based alpha-beta filter for vessel trajectory prediction, addressing the limitations of conventional static parameter tuning. The proposed method dynamically adjusts alpha and beta parameters using fuzzy logic and processes speed, heading, latitude, and longitude inputs to predict trajectories over extended periods. The fuzzy alpha-beta filter’s performance was evaluated on AIS data from 50 ships using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), Non-Linear ADE (NL-ADE), and$R^{2}$. It achieved superior accuracy (ADE: 0.020, FDE: 0.031, NL-ADE: 0.011,$R^{2}$: 0.97) compared to conventional and deep learning models, demonstrating its efficacy in complex maritime scenarios. These findings mark a significant advancement in trajectory prediction systems, enhancing maritime safety and operational efficiency.

Efficient energy utilization while ensuring occupants’ comfort remains a primary objective in smart home environments. This study presents a novel smart home management methodology that integrates Deep Neural Networks (DNNs) with an optimization algorithm to enhance energy efficiency and user comfort. Unlike traditional approaches, which often focus on individual aspects of energy management or user comfort, our method comprehensively addresses both aspects by considering key environmental factors such as air quality, temperature, and illumination. By employing specialized DNNs, our approach dynamically adjusts home conditions to align with user preferences, thereby enhancing overall comfort and satisfaction. The primary innovation of our methodology lies in the Comfort Index Calculation System, which utilizes advanced DNNs to precisely quantify user comfort based on environmental conditions. This system enables our optimization algorithm to iteratively adjust environmental settings, striking a balance between user comfort and energy consumption. By continuously learning from user feedback and environmental data, our integrated approach ensures that smart homes adapt to occupants’ evolving preferences and external conditions, thereby offering a more personalized and responsive living environment. Our results demonstrate the capability of this integrated approach in improving smart home environments, offering significant implications for environmental living and smart building automation. By bridging the gap between energy efficiency and user comfort, our study contributes to advancing the field of smart home management, ultimately realizing the full potential of energy-efficient and comfortable living environments.

Epilepsy is a prevalent neurological disorder that affects various aspects of an individual’s life, including economic, social, and biological aspects. Despite medication, many individuals with epilepsy suffer from uncontrolled seizures and the side effects of anticonvulsants. The condition also causes difficulties with information processing speed, memory, and individual attention, which may be due to the seizures, underlying causes of the condition, or the drugs used to treat it. This paper describes the preparation and development of a novel video-based method for future research on epileptic seizures in mice. Seizures were induced in mice using pilocarpine hydrochloride (100-160 mg/kg) and electric shock via ear-clip electrodes (5 mA, 60 Hz, 0.2 s stimulus duration). A 20-second video was recorded for each scenario, and then labeling was performed for each frame (600 frames in a 20-second clip) using Labelbox software for 15 entities in the mice, including the head, nose, shoulder, pelvis, tail0, tail1, tail2, arm-L0, arm-L1, arm-R0, arm-R1, leg-L0, leg-L1, leg-R0, and leg-R1.

In this paper, we have developed two neural network-based algorithms: the neural network-based Kalman filter (KF) algorithm and the neural network-based alpha-beta (α-β) filter algorithm. These algorithms incorporate a neural network to improve prediction accuracy and performance. Both algorithms consist of three input layers (temperature sensor, humidity sensor, and sensor readings), 15 hidden layers, and different output layers. The KF algorithm has a single output layer, while the alpha-beta filter algorithm has two output layers. These output layers dynamically interact with the KF algorithm and α-β filter to predict the final output values. For the KF algorithm, we consider two factors: R computation and the noise factor F. To evaluate the performance of these algorithms, we utilize the root mean square error (RMSE). The sensor readings for both algorithms are relatively high, specifically 5.215. Through the neural network-based alpha-beta filter, we achieved a minimum error of 3.21. In the case of the neural network based Kalman filter, we obtained the best-case result of 2.41 with R=14 and F=0.01. The proposed neural network-based system yields improved results compared to the simple Kalman filter and alpha-beta filter algorithms.