Recent Advances in Computer Science and Communications - Volume 19, Issue 2, 2026

In the field of digital forensics, the proliferation of Internet of Things (IoT) devices within intelligent residences has presented both new opportunities and challenges. Every gadget, including smart thermostats, security cameras, lighting controls, and even washing machines or refrigerators is equipped by these installers in the range of gadgets offered by manufacturers.

This research conducts a comprehensive investigation and classification of contemporary forensic tools designed for IoT devices in smart homes, highlighting their characteristics, methods of data collection, types of target devices, analytical methodologies, practical applications, and capabilities for integration. This generally involves a comparison of different forensic products or solutions and evaluation on various criteria such as cost, supportability, maintainable architecture-designs (integration), speedy acquisition speed/performance effectiveness without compromising quality, ease-of-use, and consistency.

A comparative analysis with detailed tables & radar charts identifying the detailed pros and cons of each tool, our findings help forensic professionals understand when to use them for effective decisions. The results show that XRY and UFED by Cellebrite both scored 5/5 in each criterion, showing the best performance in mobile device forensics. Wireshark and tcpdump also have high rates for the accuracy and reliability criteria, with results of 5/5, and are therefore also highly recommended in the area of analysis of network traffic. Magnet AXIOM and NetworkMiner graded evenly well, with a usability rating of four out of five and an integration mark of 4 out of five, which diversified them for computer and mobile forensics. Splunk and ELK Stack scored topping the scalability category, with each scoring out of five, which further confirmed the analysis of logs well for large data sets. These numerical results further underline that the choice of the tool depends on specific forensic requirements.

The authors examine future IoT forensics in smart homes which highlights the necessity of devices working with each other through a standard and sophisticated analysis to deal with dynamic complexity development within this field.

Unmanned aerial vehicles (UAVs) are low-cost, easy to deploy, and can be integrated with IoT to solve human-related problems. They have applications in military and civil sectors, such as forest fire detection, sports monitoring, agriculture, and border surveillance. UAV-based applications can be signal or multi-system.

This work is an attempt to present routing protocols within FANET. AntHocNet has shown better results than traditional routing protocols. While basic principle of ant colony optimization is used in the proposed algorithm. Engineers face a lot of problems due to the dynamic behavior of UAVs. Traditional routing protocols are compared with the proposed algorithm in simulation results. Gauss-Markov mobility model is used which easily covers temporal dependencies. Quality of experience parameters are utilized to check routing protocols' performance. More interestingly, various applications of UAVs are discussed in detail.

Unbalanced communication in UAVs directly disturbs the entire network.

Therefore, routing protocols are introduced in UAV-to-UAV communication. During simulation packet loss, throughput, end-to-end delay, bandwidth utilization, packet drop rate, and packet delivery parameters are used.

Traffic prediction is a key component of the intelligent transportation system for researchers and practitioners. It is extremely challenging because traffic flows typically exhibit complicated patterns, complex spatio-temporal correlations, and non-linearities.

Prediction of traffic density on the roads can help not only urban traffic management but also support for other road services such as path planning.

In this paper, we propose an attention-based graph convolutional network (AGCN) model to solve the traffic prediction problem. The primary focus of AGCN is on temporal, daily, and weekly dependencies of traffic periodicity. To efficiently capture the dynamic geographical and temporal correlations in traffic data, an attention-based spatial-temporal mechanism is employed. Additionally, standard convolutions are employed to extract temporal data and graph convolutional networks are used to capture spatial patterns.

The final prediction results are generated by fusing the outputs of these components. California Transportation Agencies Performance Measurement System (CalTrans PeMS) dataset is used in this research to assess the performance. The proposed model has been validated using simulations that exhibit the viability of the method and show 4% increase in the accuracy of prediction.

For improved route planning and to arrive at the destination in the least amount of time, an efficient traffic pre- diction model is suggested. This enhances overall transportation system efficiency and aids in traffic control.

Unbalanced datasets present a significant challenge in machine learning, often leading to biased models that favor the majority class. Recent oversampling techniques like SMOTE, Borderline SMOTE, and ADASYN attempt to mitigate these issues. This study investigates these techniques in conjunction with machine learning models like SVM, decision tree, and logistic regression. The results reveal critical challenges such as noise amplification and overfitting, which we address by refining the oversampling approaches to improve model performance and generalization.

In order to address this challenge of unbalanced datasets, the minority class is oversampled to accommodate the majority class. Oversampling techniques such SMOTE (Synthetic Minority Oversampling Technique), Borderline SMOTE and ADASYN (Adaptive Synthetic Sampling) are used in this work.

To perform the comprehensive analysis of various oversampling methods for taking care of class imbalance issue using ML methods.

The proposed methodology uses BERT technique which removes the pre-processing step. Various proposed oversampling techniques in the literature are used for balancing the data, followed by feature extraction and text classification using ML algorithms. Experiments are performed using ML classification algorithms like Decision tree (DT), Logistic regression (LR), Support vector machine (SVM) and Random forest (RF) for categorizing the data.

The results show improvement corresponding SVM using Borderline SMOTE, resulting in an accuracy of 71.9% and MCC value of 0.53.

The suggested method assists in the evolution of fairer and more effective ML models by addressing this basic issue of class imbalance.

Pest detection plays a crucial role in smart agriculture; it is one of the primary factors that significantly impact crop yield and quality.

In actual field environments, pests often appear as dense and small objects, which pose a great challenge to field pest detection. Therefore, this paper addresses the problem of dense small pest detection.

We combine a pyramid vision transformer and prime sample attention (named PVT-PSA) to design an effective pest detection model. Firstly, a pyramid vision transformer is adopted to extract pest feature information. Pyramid vision transformer fuses multi-scale pest features through pyramid structure and can capture context information of small pests, which is conducive to the feature expression of small pests. Then, we design prime sample attention to guide the selection of pest samples in the model training process to alleviate the occlusion effect between dense pests and enhance the overall pest detection accuracy.

The effectiveness of each module is verified by the ablation experiment. According to the comparison experiment, the detection and inference performance of the PVT-PSA is better than the other eleven detectors in field pest detection. Finally, we deploy the PVT- PSA model on a terrestrial robot based on the Jetson TX2 motherboard for field pest detection.

The pyramid vision transformer is utilized to extract relevant features of pests. Additionally, prime sample attention is employed to identify key samples that aid in effectively training the pest detection models. The model deployment further demonstrates the practicality and effectiveness of our proposed approach in smart agriculture applications.

With the rapid development of society, motor vehicles have become one of the main means of transportation. However, as the number of motor vehicles continues to increase, traffic safety accidents also continue to appear, bringing serious threats to people's lives, and property safety. Fatigue driving is one of the important causes of traffic safety accidents.

To address this problem, a target detection algorithm called VA-YOLO is designed to improve the speed and accuracy of facial recognition for fatigue checking. The algorithm employs a lightweight backbone network, VanillaNet, instead of the traditional backbone network, which reduces the computational and parametric quantities of the model. The SE attention mechanism is also introduced to enhance the model's attention to the target features, which further improves the accuracy of target detection. Finally, in terms of the bounding box regression loss function, the SIoU loss function is used to reduce the error.

The experimental results show that, compared to YOLOv8n, the VA-YOLO algorithm improves the accuracy by 1.3% while the number of parameters decreases by 30%.

This shows that the VA-YOLO algorithm has a significant advantage in realizing the balance between the number of parameters and accuracy, which is important for improving the speed and accuracy of fatigue driving detection.