Ahmed Imteaj
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View article: Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models
Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models Open
The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve…
View article: A Comprehensive Survey of Large Language Models for Human Activity Recognition: Opportunities, Challenges, and Future Directions
A Comprehensive Survey of Large Language Models for Human Activity Recognition: Opportunities, Challenges, and Future Directions Open
View article: Digital Forensic Investigation of the ChatGPT Windows Application
Digital Forensic Investigation of the ChatGPT Windows Application Open
The ChatGPT Windows application offers better user interaction in the Windows operating system (OS) by enhancing productivity and streamlining the workflow of ChatGPT's utilization. However, there are potential misuses associated with this…
View article: Benchmarking Large-Language Models for Resource-Efficient Medical AI for Edge Deployment
Benchmarking Large-Language Models for Resource-Efficient Medical AI for Edge Deployment Open
Large-Language Models (LLMs) are rapidly emerging as transformative tools across diverse domains, leveraging extensive training on vast and heterogeneous datasets to capture nuanced knowledge and transcend traditional boundaries of underst…
View article: Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks
Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks Open
The rapid proliferation of speech-enabled technologies, including virtual assistants, video conferencing platforms, and wearable devices, has raised significant privacy concerns, particularly regarding the inference of sensitive emotional …
View article: Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing Open
Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model generated utilizing the local computational power is transmitted…
View article: Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks Open
Large Vision-Language Models (LVLMs), trained on multimodal big datasets, have significantly advanced AI by excelling in vision-language tasks. However, these models remain vulnerable to adversarial attacks, particularly jailbreak attacks,…
View article: TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency Open
The rapid advancement and increasing complexity of pretrained models, exemplified by CLIP, offer significant opportunities as well as challenges for Federated Learning (FL), a critical component of privacy-preserving artificial intelligenc…
View article: Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models
Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust and Semantically-Rich Vision-Language Models Open
Vision-language models (VLMs) have achieved significant strides in recent times specially in multimodal tasks, yet they remain susceptible to adversarial attacks on their vision components. To address this, we propose Sim-CLIP, an unsuperv…
View article: Sticks and stones may break my bones, but words will never hurt me!—Navigating the cybersecurity risks of generative AI
Sticks and stones may break my bones, but words will never hurt me!—Navigating the cybersecurity risks of generative AI Open
View article: Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning Open
The widespread adoption of machine learning (ML) across various industries has raised sustainability concerns due to its substantial energy usage and carbon emissions. This issue becomes more pressing in adversarial ML, which focuses on en…
View article: From Early Adoption to Ethical Adoption: A Diffusion of Innovation Perspective on ChatGPT and Large Language Models in the Classroom
From Early Adoption to Ethical Adoption: A Diffusion of Innovation Perspective on ChatGPT and Large Language Models in the Classroom Open
This paper presents a comprehensive framework and actionable recommendations for the ethical integration of ChatGPT and other Large Language Models (LLMs) into the academic environment from a Diffusion of Innovation Theory perspective. It …
View article: FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer Open
Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained comm…
View article: A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing Open
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generati…
View article: A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems
A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems Open
The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy…
View article: Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System Open
Human Activity Recognition (HAR) is a problem of interpreting sensor data to human movement using an efficient machine learning (ML) approach. The HAR systems rely on data from untrusted users, making them susceptible to data poisoning att…
View article: Examining Mental Disorder/Psychological Chaos through Various ML and DL Techniques: A Critical Review
Examining Mental Disorder/Psychological Chaos through Various ML and DL Techniques: A Critical Review Open
Mental soundness is a condition of well-being wherein a person understands his/her potential, participates in his or her community and is able to deal effectively with the challenges and obstacles of everyday life. It circumscribes how an …
View article: An Approach to Digitalize the Health Care System of Bangladesh using Smartphone
An Approach to Digitalize the Health Care System of Bangladesh using Smartphone Open
Health is one of the basic needs of human life. If this need cannot be fulfilled, then nothing can compensate for the loss. Often in Bangladesh, we observe that the economic ability of mass people can hardly ensure proper health care. The …
View article: Leveraging asynchronous federated learning to predict customers financial distress
Leveraging asynchronous federated learning to predict customers financial distress Open
In recent years, as economic stability is shaking, and the unemployment rate is growing high due to the COVID-19 effect, assigning credit scoring by predicting consumers’ financial conditions has become more crucial. The conventional machi…
View article: An enhanced method of initial cluster center selection for K-means algorithm
An enhanced method of initial cluster center selection for K-means algorithm Open
Clustering is one of the widely used techniques to find out patterns from a dataset that can be applied in different applications or analyses. K-means, the most popular and simple clustering algorithm, might get trapped into local minima i…
View article: FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures
FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures Open
Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures…
View article: Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic
Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic Open
SARS-CoV-2 (COVID-19) is a new strain of coronavirus that is regarded as a respiratory disease and is transmittable among humans. At present, the disease has caused a pandemic, and COVID-19 cases are ballooning out of control. The impact o…
View article: FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment
FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment Open
Federated Learning (FL) is a recently invented distributed machine learning technique that allows available network clients to perform model training at the edge, rather than sharing it with a centralized server. Unlike conventional distri…
View article: FedAR: Activity and Resource-Aware Federated Learning Model for\n Distributed Mobile Robots
FedAR: Activity and Resource-Aware Federated Learning Model for\n Distributed Mobile Robots Open
Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices\nare considered the primary data source for a distributed network. Due to a\nrevolutionary breakthrough in internet availability and continuous improvement\nof the …
View article: FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots
FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots Open
Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT…
View article: Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks
Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks Open
This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires. A recent example is PG&E's forced power outage to contain wildfire …
View article: Interdependent Networks: A Data Science Perspective
Interdependent Networks: A Data Science Perspective Open
Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of inte…
View article: Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art Open
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing devices. As a result, vast amounts of data are generated on e…
View article: Leveraging Decentralized Artificial Intelligence to Enhance Resilience\n of Energy Networks
Leveraging Decentralized Artificial Intelligence to Enhance Resilience\n of Energy Networks Open
This paper reintroduces the notion of resilience in the context of recent\nissues originated from climate change triggered events including severe\nhurricanes and wildfires. A recent example is PG&E's forced power outage to\ncontain wildfi…
View article: Construction of Single Axis Automatic Solar Tracking System
Construction of Single Axis Automatic Solar Tracking System Open
Solar power is the transformation of daylight into power, either straightforwardly utilizing photovoltaic (PV), or in a roundabout way controlling concentrated sun powered force (CSP).Concentrated sun powered force frameworks use lenses or…