Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Abnormal transmission signal is the main reason for serious uplink transmission interference in ultra-dense networks, which leads to insufficient transmission interference suppression effect. Therefore, a local weighted regression based uplink transmission interference suppression for ultra-dense networks is proposed. Firstly, the robust local weighted regression is used to analyse the abnormal transmission signals of the uplink transmission link in the ultra-dense network, and to improved the suppression effect of the uplink transmission interference. Secondly, the adaptive time-frequency analysis is used to extract the characteristics of abnormal signals and determine the existence of uplink transmission interference. Finally, the residual neural network is used to identify the interference signal, and the interference reconstruction and suppression are combined to achieve the interference suppression of the uplink transmission in the ultra-dense network. Experimental results show that the reference signal received power gain, signal-to-noise ratio, and information average rate gain of the uplink station are optimised with the proposed interference suppression, and the maximum received power gain is up to 53.79 dB. Compared with the existing methods, the packet loss rate of the proposed method is significantly reduced, and the packet loss rate remains below 1%.

The rapid advancement of software and AI has led to the proliferation of deepfake media, raising ethical concerns due to its potential for manipulation and deception. We propose a hybrid model combining Inception ResNetV2 and Xception architectures, leveraging LSTM and CNN for precise deepfake detection. With a diverse dataset, our study aims to identify and mitigate the harmful effects of deepfake technology, including financial fraud and misinformation dissemination. Through rigorous training, our model achieves an impressive accuracy of over 96.75% in detecting deepfake videos. This represents a significant step forward in combating deceptive content and upholding authenticity in the digital sphere. Our research emphasises the societal importance of deepfake detection methods, contributing to the broader mission of fostering trust and accountability online.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

In this study, we propose a generative adversarial network (GAN) framework for music style transfer. Initially, a dataset of traditional Jiangnan songs is pre-processed into two categories: complete compositions and corresponding musical phrases (starting and ending notes), which are then converted into piano-roll images. The CycleGAN model is then used to train these images until the model converges to establish a music style transfer model. The goal is to allow users to input only the starting and ending notes of each measure as a musical phrase, and the system will convert this phrase into complete musical compositions in the Jiangnan style. Then we use a deep learning framework and music expertise for data processing, enhancing the quality and utility of our conversions. At the same time, we have established music style assessment metrics based on the statistical data of the dataset, providing an effective method for evaluating music styles.

SUM queries are one of the most important query services in outsourced encrypted databases. Many current traditional solutions rely on homomorphic encryption (HE) algorithms to perform additional calculations on encrypted data. However, the query efficiency is extremely low and the performance is poor. To address these issues, this paper proposes THS, an efficient secure database SUM query solution. The solution first establishes a B+ tree index structure and adds a field to each non-leaf node of the B+ tree to store the sum of all child nodes for that node. The solution also incorporates an efficient HE algorithm, enabling direct summation calculations and reducing computation pressure. Our solution achieves a time complexity of O(1) for SUM queries and can be flexibly integrated into any encrypted database. In scenarios with frequent SUM queries, comparative analysis with other solutions currently demonstrates that THS is significantly superior in various dimensions.

Botnets represent a significant threat to the security of the current internet network environment. They can be employed to carry out various malicious activities, not only impairing the performance and security of individual devices but also causing extensive damage to the entire network. Therefore, efficient detection of botnets is essential for network security. In recent years, the mainstream approach to botnet detection has involved the use of graph neural network (GNN) models, aiming to fully utilise the graph-structured properties of network data. However, current GNN methods frequently struggle with complicated structures and lack interpretability. To address these issues, we have designed a new model based on graph isomorphism networks (GINs). This model significantly simplifies the complexity without compromising detection accuracy and introduces explainable techniques for analysing model weights and conducting subgraph mining. By leveraging the concept of graph isomorphism, our method can more precisely characterise and identify botnet features. Experimental results demonstrate that our model excels in both accuracy and interpretability, making it highly valuable for real-world network environment applications.

6D object pose estimation, crucial for applications such as scene understanding, AR/VR, and robotic grasping, focuses on determining an object's rotation and translation from single-view images. Despite advancements in 3D deep learning, existing methods still struggle with large shape variations, high training demands, and unseen poses. This paper addresses these issues by introducing a rotation-invariant neural network. We propose a rotation-invariant 3D convolutional network that processes a point cloud to predict per-point canonical coordinates, from which the 6D pose, is estimated. The network utilises relative distances and angles within a representative point set. Experiments on a public dataset show that our method outperforms several state-of-the-art baselines, excelling in handling novel poses and severe occlusions. Ablation studies further highlight the importance of the individual components.

The energy internet (EI) is a cutting-edge technology with a vision to integrate diverse energy sources into an efficient and flexible grid. However, there are several critical security challenges for its real-life implementation due to its interconnected nature. Again, the advancement of another cutting-edge technology, quantum computing, heightens these concerns as it threatens the traditional cryptographic defences, making them vulnerable to quantum attacks. In this work, we introduce a secure consensus mechanism for IoT-based EI systems by integrating post-quantum cryptography (PQC) and blockchain technology. In our approach, we combine the strengths of two state-of-the-art protocols: a mutual authentication framework and a post-quantum consensus mechanism. We propose two secure consensus algorithms, leveraging the Dilithium and Falcon PQC schemes. With appropriate simulation studies, we show that the results demonstrate that our proposed approach significantly improves transaction throughput and reduces latency, providing a resilient framework for efficient and secure energy data management in a post-quantum world.