3066-280XISSN (Online)
This work examines the transformational potential of AI-based decentralized energy systems: P2P renewable energy networks interconnect AI, blockchain technology, and multi-agent systems, thus circumventing the barriers of traditional centralized grids. This paper will trace how their latest trends in real-time energy optimization, secure smart contracts, and autonomous coordination of distributed resources can enhance grid resilience, minimize transmission losses, and democratize energy markets. However, it becomes evident that to enable mass adoption; significant challenges must be addressed regarding renewable energy intermittency, scalability limitations, regulatory loopholes, and cybersecurity threats. Through synthesizing current research and the analytical case of Brooklyn Microgrid, this paper discusses some of the barriers and potential future directions that must be emphasized, such as hybrid optimization models, standardized frameworks, and inclusive design for accelerating transitions towards sustainable and equitable energy systems.
Read MoreDoi: https://doi.org/10.54216/MOR.040101
Vol. 4 Issue. 1 PP. 01-11, (2025)
Adversarial deep learning has, therefore, been tabled as one of the key research focus areas in neurosciences, and both the opportunities and drawbacks for the operation of deep learning models on neuroimaging and diagnostic jobs have been unveiled. This review examines these models' weaknesses from adversarial attacks, which can severely affect diagnosis and patient care. For example, it has been shown that slight disturbances in the level of EEG signals can confuse more profound learning algorithms employed for the identification of epilepsy, which can lead to severe diagnostic mistakes. In addition, GANs have the dual role of generating realistic neuroimaging data that can improve diagnostic processes while at the same time using adversarial images that expose the deficits of current models. This duality highlights the need to securely defend models against such risks and employ adversarial training and bio-mimic-based resilient neural network techniques. The consequence of these discoveries should not be underestimated because they reveal the necessity of showing further safety in using deep learning techniques in clinical practices. In addressing these weaknesses, the principle goal of this research is not only to help improve the diagnostic systems but also to expand the knowledge on how adversarial deep learning might affect the health, well-being and safety of patients in neuroscience.
Read MoreDoi: https://doi.org/10.54216/MOR.040102
Vol. 4 Issue. 1 PP. 12-20, (2025)
The progress of neuroimaging and the availability of big neuro data have brought both opportunities and difficulties in the fast-developing scientific area of computational neuroscience. As this review will show, new ways of managing and analyzing these large and layered datasets are emerging, highlighting the importance of various computational approaches to achieve valuable insights. We assess various methods for performing such analyses, among which we focus on machine learning algorithms like deep learning capable of addressing high-dimensional data characteristics for neuroimaging studies. The proposed method of analyzing multiple structural and functional MRI data in conjunction with electrophysiological and genetic data should help model neurological disorders more accurately. We also describe the preprocessing methods for dealing with data noise and variability, combined with statistical analysis that depends on existing databases to identify previously unknown patterns concerning brain functions and disorders. We also discussed the importance of open-source teamwork spaces and applications, which allow datasets and results to be shared and replicated. This review, therefore, aimed at reviewing the most effective strategies and filling the gaps within the current methodologies that may help enhance the strength and reliability of vast neurological datasets, hence diminishing diagnostic errors and helping formulate the right therapeutic intercessions in neurological disorders. This synthesis emphasizes the choice of a multidisciplinary approach when studying the neural tissues since the issue appears complex.
Read MoreDoi: https://doi.org/10.54216/MOR.040103
Vol. 4 Issue. 1 PP. 21-30, (2025)
Artificial intelligence systems are revolutionizing how industries reduce carbon dioxide emissions in numerous business fields. This study combines research on how artificial intelligence merges with carbon reduction methods, specifically in industrial procedures and electric vehicle manufacturing, with an environmental sustainability focus. Multiple empirical studies and advanced AI models provide insight into sustainability effects caused by AI systems and emission decrease processes. AI technology performs three essential functions to enhance energy optimization pro, mote eco-friendly research, and improve environmental prediction accuracy. The identified information provides essential guidance to policymakers and industrial leaders about AI applications for achieving zero emissions and sustainability targets. The review presents evidence that AI technology can redefine sustainability throughout vehicle production while managing transportation and other fields thus helping solve escalating climate issues and drive eco-friendly developments.
Read MoreDoi: https://doi.org/10.54216/MOR.040104
Vol. 4 Issue. 1 PP. 31-40, (2025)
The increasing number of cyber security threats, notably ransomware and malware, make traditional methods ineffective, hence the need for intelligent methods. This literature review delves into the latest advancements in cyber security technologies that leverage artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance system defenses. Key focus areas include improving ransomware detection, developing more effective intrusion detection systems (IDS), securing Internet of Things (IoT) networks, and strengthening cryptographic methods. The reviewed studies highlight how AI-driven techniques—such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and adversarial training—automate the detection of threats, optimize cyber security measures, and offer real-time responses to evolving risks. Innovative frameworks like Zero Trust Architecture (ZTA) and AI further bolster security by offering automated threat mitigation and anomaly detection. Furthermore, new metaheuristic algorithms are integrated into IDS systems to enhance the detection rate and minimize false positives. The advanced approaches show how AI could solve the constantly emerging challenges in cyber security and focus on a continuous development approach to make cyber security scalable, robust, and transparent when considering complex attacks.
Read MoreDoi: https://doi.org/10.54216/MOR.040105
Vol. 4 Issue. 1 PP. 41-49, (2025)