This study presents a comprehensive UAV-based inspection system that leverages AI, high-resolution imaging, thermal sensors, and LiDAR for efficient and automated monitoring of power transmission lines and solar farms. By integrating real-time cloud analytics and AI-driven fault detection, the research significantly enhances inspection speed, safety, and diagnostic accuracy. It offers a scalable and cost-effective alternative to traditional methods, contributing to the development of smart, resilient, and sustainable energy infrastructure.
Deepfake Detection: A Hybrid Deep Learning Approach Using ResNext and LSTM Models
Deepfake technology, leveraging advancements in deep learning, has become a significant threat to digital media authenticity, enabling the creation of hyper-realistic yet deceptive videos that challenge existing detection methods. This paper presents a hybrid approach combining ResNext Convolutional Neural Networks (CNN) for frame-level feature extraction and Long Short-Term Memory (LSTM) networks for analyzing temporal dependencies to improve deepfake detection accu racy. The study utilized a balanced dataset comprising videos from FaceForensic++, Celeb-DF, and custom-crafted deepfakes, with preprocessing steps that included facial region cropping, frame standardization, and noise reduction. The proposed model achieved an accuracy of 94.87%, outperforming existing methods by effectively capturing both static and dynamic video fea tures. Key innovations include leveraging the complementary strengths of CNNs and LSTMs to address frame-level and se quential inconsistencies in fake media. This approach is validated through extensive experimentation, demonstrating robustness against evolving generative adversarial techniques. The results establish a strong foundation for scalable and real-time detection applications, with future work aiming to enhance detection for multi-modal data and improve computational efficiency for deployment in resource-constrained environments.
Efficient Deepfake Video Detection Using ResNext CNN and Temporal LSTM Networks
Since deepfake films allow for the production of extremely convincing manipulated media, they represent serious threats to the integrity of information. These videos are produced utilising sophisticated machine learning models such as Gen erative Adversarial Networks (GANs). This study introduces a hybrid deep learning framework that efficiently detects deepfakes by combining Long Short-Term Memory (LSTM) networks for temporal analysis with ResNext Convolutional Neural Networks (CNNs) for spatial feature extraction. By applying transfer learning, the model reduces computing overhead while achieving great accuracy and efficiency. For training and assessment, a meticulously selected dataset of 1,000 videos that was evenly dis tributed between authentic and fraudulent content was utilised. During preprocessing, video frames’ facial features were sepa rated and cropped to provide a high-quality face-only dataset. The suggested model proved its resilience in detecting modified information with an astounding 95% detection accuracy on the test set. The model’s superiority over baseline techniques was demonstrated through performance validation using metrics like precision, recall, and F1-score. In order to combat the swift advancement of deepfake technology, this study highlights the significance of creating flexible detection methods. Subsequent efforts will concentrate on extending detection capabilities to encompass full-body movements and incorporating the frame work into easily available tools such as browser-based plugins for continuous use.
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
We introduce PotatoGANs, a hybrid augmentation approach using CycleGAN and Pix2Pix to generate synthetic diseased potato images from healthy samples, enhancing dataset diversity and model generalization while reducing data collection costs. To support model interpretability, we combine GradCAM, GradCAM++, and ScoreCAM with DenseNet169, ResNet152 V2, and InceptionResNet V2, offering transparent visual explanations of model predictions. Unlike existing work focused solely on leaf-level analysis, our method addresses whole-crop disease localization using advanced segmentation tools like Detectron2. Validated by the Bangladesh Agricultural Research Institute, this study aims to support the advancement of agricultural disease diagnosis and management in Bangladesh.
Green Hydrogen & Low Carbon Concrete for Circular Economy at South Sulawesi, Indonesia
By implementing these recommendations, South Sulawesi can position itself as a leader in sustainable industrial development, contributing to global efforts to combat climate change and promote circular economy principles.
Sustainable Energy for Port Construction with Low Carbon Concrete from Industrial Symbiosis at WESTPORT Kwinana & BANTAENG Sulawesi
This paper signifies the importance of replacing current Ordinary
Portland Cement (OPC) manufacturing processing with low carbon emission geopolymer based cements in construction industry and addressing the challenges for the supply chain in Australia.
Sustainable Energy for Port Construction with Low Carbon Concrete from Industrial Symbiosis at WESTPORT Kwinana & BANTAENG Sulawesi
At Bantaeng in South Sulawesi and Kwinana in
Western Australia new industrial scale ports will be built to serve
the industrial precincts at these locations. At both these sites a 1-
2Mtpa GPC plant is proposed for precast production of some 1,600
port modules as well as other infrastructure requiring some 750,000
cum of concrete and thereafter the plant can be repurposed for other
products for local markets such as reef modules and wall panels.
Geopolymer concrete can be the replacement for conventional
concrete and be made from waste-derived materials while having a
lower carbon footprint. The plant is designed to be operated by
renewable energy and an energy audit estimated that a 1Mtpa
geopolymer production plant needs up to 200 GWh pa to operate.
This could be served by 6-10 on-land wind turbines combined with
solar PV farm at a total cost $45-55 million USD. The electricity
generated @ say $100/MWh was worth $12-20M pa that could
result in a payback of 2-5 years. In Kwinana, planning is already
underway for a large wind farm as part of the overall
decarbonisation strategy for this industrial precinct. Feedstock
materials can be harnessed for use in the geopolymer production
plant by means of Circularity Hubs. These hubs can be established
through the KIC4 and 6-Capitals models of Industrial Symbiosis and
to optimise the proposed geopolymer plant within the industrial
precincts at Bantaeng and Kwinana. Such an approach can
contribute to Regenerative Development when both of the ports are
built.
Potential of seaweed in Indonesia as an alternative iodine source
Diversification of seaweed products can also open up new economic opportunities, create jobs, and reduce dependence on imported products. As an archipelagic country rich in water resources, Indonesia has unique characteristics in the water-energy-food relationship. Additional investigation is required to explore the most effective types of seaweed and optimal processing methods to maintain iodine content and other bioactive components.