Deep learning classifiers trained on structural magnetic resonance imaging (MRI) are increasingly proposed for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), yet the pipelines that produce these models depend on shared datasets, pre-trained weights, and federated updates that an advers...
Adversarial threats like data poisoning attacks affect the training datasets and create biased, worse, or malicious AI models. Static heuristics and high false positive rates have impeded traditional defenses against such adaptive stealthy attacks. These defenses also do not generalize well in federated and non-IID set...
Nitesh L. Hatwar, V. K. Sharma, Bhushan Manjre· ASEAN Journal of Scientific...· 0 citations
A literature review of Machine Learning applications in the domain of Electricity Theft Detection (ETD) that is grounded in 71 scholarly studies produced between 2021 and 2026 and selected according to criteria of machine learning application in the process of theft/NTL detection and accompanying empirical validation.
Oladipupo Olatunji, Victor Edibo, Olumide Alamu· Journal of Science Research...· 0 citations
A novel adaptive detection framework that combines blockchain-based integrity verification with explainable artificial intelligence to identify and mitigate poisoning attempts in federated learning systems for electric vehicle charging networks is presented.
Milad Rahmati, Nima Rahmati· Journal of Electrical System...· 0 citations
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Background: Parallel and distributed execution is proposed to relieve throughput, trainability and resource constraints in variational quantum algorithms (VQAs), but the label “parallel” covers physically and statistically different interventions. We assessed what has actually been measured on noisy intermediate-scale...
Wael Al-Thohli· Zenodo (CERN European Organi...· 0 citations
Brain tumor classification categorizes tumor types from medical images to support diagnostic decisionmaking.Accurate brain tumor classification remains challenging because privacy restrictions prevent hospitals from sharing raw patient data.Consequently, this lack of cross-institutional model training affect classifica...
Pranusha Reddy Kallu, Geetha Soman, P. S. Rajakumar· International Journal of Int...· 0 citations
In the fields of emergency communication and disaster rescue, satellite plays a pivotal role due to its extensive coverage and robust resistance to damage, serving as a critical support mechanism in extreme environments. However, the traditional satellite data processing paradigm exhibits significant limitations in ter...
Yun-Na Li, Jia-Xing Wang, Yu-Zhe Sun et al.· Global Intelligent Industry...· 0 citations
Software archive accompanying the paper "Who Benefits from Federation? Heterogeneous Gains in Cross-Operator Network Traffic Representation Learning" by Mohammad Arafath Uddin Shariff and Byrav Ramamurthy (School of Computing, University of Nebraska-Lincoln), presented at the 13th International Workshop on Innovating t...
M A U Shariff· Zenodo (CERN European Organi...· 0 citations
Crop diseases constrain global agricultural productivity and food security, particularly for smallholder farmers who lack timely, affordable diagnostic tools. Artificial intelligence and machine learning have transformed automated crop disease detection, but much reported performance rests on controlled laboratory data...
Istifanus,, S.D., D. A. Adedokun, I. J. OGWU et al.· Journal of Biotechnology and...· 0 citations
Background: Healthcare databases in the United States, including electronic health record systems, claims repositories, imaging archives and research data warehouses, have become high-value targets for ransomware, credential theft, insider misuse and privacy attacks. Artificial intelligence and machine learning are wid...
Chinyere Nelson Amaeze, Ezekiel Dauda Gambo· Journal of Scientific Resear...· 0 citations
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
