In the past few decades, cutting-edge information and communication technology has been used in several ways to keep an eye on the marine environment. Underwater wireless sensor networks (UWSNs) can measure the amount of water and soil conditions, such as soil salinity, moisture, and movements, to predict landslides. UWSNs are made up of many wireless underwater sensor nodes (WSNs) that are spread out across the thalassic environment. These networks have several uses, including data collection, navigation, resource analysis, surveillance, disaster prediction, etc. Nowadays, energy efficiency becomes a complex issue to handle in the design of the UWSN due to the limited battery capacity and the challenges associated with changing or charging the integrated batteries. According to previous research, clustering and routing have already been effective methods of improving energy efficiency in the UWSN, as unreplaceable batteries and long-distance communication delays are particularly vulnerable. As a result, one of the UWSN’s critical issues is determining how to extend the network’s lifespan while balancing its energy consumption and shortening transmission distances. In UWSN clustering, the most important considerations are acquiring a suitable count of clusters, constituting the clusters, and picking the most satisfactory cluster head (CH) for each cluster. Based on several factors, such as residuary energy, total energy consumption, and other considerations, our proposed approach picks CHs and arranges them into clusters. Also, the proposed SS-GSO method constructs a fitness function by including various sources of information, like total energy, residual energy, and luciferin value. Several simulation runs were executed to test how much better the SS-GSO approach worked. The comparison results showed that while evaluating clustering time, our proposed SS-GSO technique performs 22.91%, 50.03%, 42.42%, 58.06% better, in case of Total energy consumption 27.02%,14%,33.76%,41.97% more energy efficient, in Cluster lifetime 9.2%,19.88%,35.91%,40.54% less and in Packet delivery rate 8.29%,14.05%,17.67%,23.97% better as compared with other heuristic techniques, such as ACO, GWO, MFO and LEACH.

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.

One of the most common types of cancer in women is cervical cancer, a disease which is the most prevalent in poor nations, with one woman dying from it every two minutes. It has a major impact on the cancer burden in all cultures and economies. Clinicians have planned to use improvements in digital imaging and machine learning to enhance cervical cancer screening in recent years. Even while most cervical infections, which generate positive tests, do not result in precancer, women who test negative are at low risk for cervical cancer over the next decade. The problem is determining which women with positive HPV test results are more likely to have precancerous alterations in their cervical cells and, as a result, should have a colposcopy to inspect the cervix and collect samples for biopsy, or who requires urgent treatment. Previous research has suggested techniques to automate the dual-stain assessment, which has significant clinical implications. The authors reviewed previous research and proposed the cancer risk prediction model using deep learning. This model initially imports dataset and libraries for data analysis and posts which data standardization and basic visualization was performed. Finally, the model was designed and trained to predict cervical cancer, and the accuracy and performance were evaluated using the Cervical Cancer dataset.

This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.

This research conducts a comparative analysis of Faster R-CNN and YOLOv8 for real-time detection of fishing vessels and fish in maritime surveillance. The study underscores the significance of this investigation in advancing fisheries monitoring and object detection using deep learning. With a clear focus on comparing the performance of Faster R-CNN and YOLOv8, the research aims to elucidate their effectiveness in real-time detection, emphasizing the relevance of such capabilities in fisheries management. By conducting a thorough literature review, the study establishes the current state-of-the-art in object detection, particularly within the context of fisheries monitoring, while discussing existing methods, challenges, and limitations. The findings of this study not only shed light on the superiority of YOLOv8 in precise detection but also highlight its potential impact on maritime surveillance and the protection of marine resources.

The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.