augmented-multimodal-fusion-for-optimized-brain-tumor-detection

Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis

Brain tumors represent a significant medical challenge, necessitating accurate and efficient detection methods for timely intervention. This work ...

Prince Sultan university

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and BAT Algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions.

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users' information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user's query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.

An SUI-based approach to explore visual search results cluster-graphs.

During unprecedented events such as COVID-19, the fabric of society comes under stress and all stakeholders want to increase the predictability of the future and reduce the ongoing uncertainties. In this research, an attempt has been made to model the situation in which the sentiment "trust" is computed so as to map the behaviour of society. However, technically, the purpose of this research is not to determine the "degree of trust in society" as a consequence of some specific emotions or sentiments that the community is experiencing at any particular time. This project is concerned with the construction of a computational model that can assist in improving our understanding of the dynamics of digital societies, particularly when it comes to the attitude referred to as "trust." The digital society trust analysis (D.S.T.A.) model that has been provided is simple to configure and simple to implement. It includes many previous models, such as standing models, Schelling's model of segregation, and tipping points, in order to construct models for understanding the dynamics of a society reeling under the effects of a COVID-19 pandemic, misinformation, fake news, and other sentiments that impact the behaviour of the different groups.

Digital society social interactions and trust analysis model.

Biometrics is the measurement of an individual's distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger's skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed  visual comparison with coherence diffusion used disjointedly and with the Gabor filter.

Fingerprint image enhancement using multiple filters.

The existing image search engines allow web users to explore images from the grids. The traditional interaction is linear and lookup-based. Notably, scanning web search results is horizontal-vertical and cannot support in-depth browsing. This research emphasizes the significance of a multidimensional exploration scheme over traditional grid layouts in visually exploring web image search results. This research aims to antecedent the implications of visualization and related in-depth browsing via a multidimensional cubic graph representation over a search engine result page (SERP). Furthermore, this research uncovers usability issues in the traditional grid and 3-dimensional web image search space. We provide multidimensional cubic visualization and nonlinear in-depth browsing of web image search results. The proposed approach employs textual annotations and descriptions to represent results in cubic graphs that further support in-depth browsing via a search user interface (SUI) design. It allows nonlinear navigation in web image search results and enables exploration, browsing, visualization, previewing/viewing, and accessing images in a nonlinear, interactive, and usable way. The usability tests and detailed statistical significance analysis confirm the efficacy of cubic presentation over grid layouts. The investigation reveals improvement in overall user satisfaction, screen design, information & terminology, and system capability in exploring web image search results.

I-Cubid: a nonlinear cubic graph-based approach to visualize and in-depth browse Flickr image results.

Intelligent Transport System (ITS) offers inter-vehicle communication, safe driving, road condition updates, and intelligent traffic management. This research intends to propose a novel decentralized "BlockAuth" architecture for vehicles, authentication, and authorization, traveling across the border. It is required because the existing architects rely on a single Trusted Authority (TA) for issuing certifications, which can jeopardize privacy and system integrity. Similarly, the centralized TA, if failed, can cause the whole system to collapse. Furthermore, a unique "Proof of Authenticity and Integrity" process is proposed, redirecting drivers/vehicles to their home country for authentication, ensuring the security of their credentials. Implemented with Hyperledger Fabric, BlockAuth ensures secure vehicle authentication and authorization with minimal computational overhead, under 2%. Furthermore, it opens up global access, enforces the principles of separation of duty and least privilege, and reinforces resilience via decentralization and automation.

BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization.

Malaria remains a formidable worldwide health challenge, with approximately half of the global population at high risk of catching the infection. This research study aimed to address the pressing public health issue of malaria's escalating prevalence in Khyber Pakhtunkhwa (KP) province, Pakistan, and endeavors to estimate the trend for the future growth of the infection.

Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends.

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation.

DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets.

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.

Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancements in artificial intelligence, particularly computer vision algorithms, have shown great potential in detecting and classifying medical images. These algorithms utilize deep convolutional neural network architectures to learn complex spatial features in images and make predictions for similar unseen images. The proposed study aims to assist gastroenterologists in making more efficient and accurate diagnoses of GI patients by utilizing its two-phase transfer learning framework to identify GI diseases from endoscopic images. Three pre-trained image classification models, namely Xception, InceptionResNetV2, and VGG16, are fine-tuned on publicly available datasets of annotated endoscopic images of the GI tract. Additionally, two custom convolutional neural networks are constructed and fully trained for comparative analysis of their performance. Four different classification tasks are examined based on the endoscopic image categories. The proposed architecture employing InceptionResNetV2 achieves the most consistent and generalized performance across most classification tasks, yielding accuracy scores of 85.7% for general classification of GI tract (eight-category classification), 97.6% for three-diseases classification, 99.5% for polyp identification (binary classification), and 74.2% for binary classification of esophagitis severity on unseen endoscopic images. The results indicate the effectiveness of the two-phase transfer learning framework for clinical use to enhance the identification of GI diseases, aiding in their early diagnosis and treatment.

A two-phase transfer learning framework for gastrointestinal diseases classification.

Retraction Note: DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets.

Fake News (FN) dissemination on websites and online platforms influences human behaviours, socio-political domains, and the sovereignty of a country. The outpour of biased news and propaganda on online portals can be addressed by restricting online propaganda using an automated mechanism. Proving the authenticity of news and information on online platforms in regional languages, such as Urdu, with limited resources and datasets, is challenging. Furthermore, limited research in resource-constrained languages has created language bias in Artificial Intelligence (AI) research, which is concentrated in this study. Natural Language Processing (NLP) techniques have been used for Fake News Detection (FND) for English news and various language-related tasks. Previous studies used Machine Learning (ML), Deep Learning (DL), and individual Pre-trained Language Models (PLMs) for Urdu FND. ML-based ensemble model showed better performance than pre-trained models for Urdu FND. We propose a methodology for Urdu FND by applying stacked ensemble learning of PLMs, ELECTRA, mBERT and XLM-RoBERTa after apposite fine-tuning and hyperparameter optimization. To overcome the limitations of each pre-trained transformer model, these are fine-tuned individually using a publicly available Urdu dataset. The prediction performance results of the proposed stacking approach surpass the performance of each pre-trained model. An Accuracy of 0.914, a Matthews Correlation Co-efficient (MCC) value of 0.898, and an F1-score of 0.904 validate the efficacy of the proposed ensemble model.

Multi-domain Urdu fake news detection using pre-trained ensemble model.

Owing to the proliferation of mobile devices, Google's Android operating system has become a dominant force in global communication. However, its popularity makes it a prime target for cyberattacks. Effective malware detection systems are crucial for combating these escalating threats, particularly amid the evolving use of adversarial examples to evade detection. These systems employ static and dynamic analysis methodologies with machine learning, particularly Generative Adversarial Networks (GANs), which play a key role. The Android Opcode Modification GAN enhances malware detection by intelligently modifying opcode distribution features using the Opcode Frequency Optimal Adjustment algorithm. Despite its effectiveness, the dual-opponent generative adversarial network (DOpGAN) introduces a grey-box attack strategy that misclassifies generated examples as benign, significantly evading detection. DOpGAN operates by altering opcode distribution features during the generation and insertion process, making it particularly challenging for detection systems to classify correctly. The adversarial examples generated by DOpGAN highlight the critical need to integrate defensive measures such as adversarial example detection systems into the Android security framework. Beyond evasion, these adversarial examples provide invaluable opportunities for retraining and improving malware detection systems, thereby ensuring their resilience against emerging threats. The findings underscore the broader need for continuous innovation in Android security mechanisms, fostering collaboration between academia and industry to protect users and systems in an ever-evolving mobile security landscape.

GEAAD: generating evasive adversarial attacks against android malware defense.

The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. However, such research tasks require large scale labelled social network data, and annotating such vast amounts of data is nearly impractical and often considered unfeasible. In addition, established literature encounters significant hurdles when it comes to detect unseen or new events, even after acquiring a substantial amount of training data. This marks a new era, emphasizing the importance of leveraging minimal data and embracing broader generalizations, much like human understanding of information. To this end, we proposed AttendFew, a model to detect events in X (formely X) with limited data (Few-shot learning) which detects events while mitigating data dependency, even for unseen events, aligning with the current need for less data and greater adaptability. The proposed method encodes the posts (also known as "posts") with BERTweet and Graph Attention Networks to capture both contextual and structural aspects of social network data and further, stacked attention will be applied to make the model attend local and global context effectively and enhances the ability to understand complex data of social network. Furthermore, the amalgamation of the feature score and Multi-layer Perceptron (MLP) facilitates class matching, aligning the derived features with their associated classifications. This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. This study represents an attempt to utilize a few-shot learning model in addressing the challenges posed by the sparsity and dynamism of online social networks for event detection.

An attention-enhanced few-shot model for event detection in online social networks.

Fake News (FN) prevalence on Online Social Networks (OSNs) and online websites is a worldwide issue. The previous studies on Fake News Detection (FND) have focused on rich-resource languages with limited relevance to users other than native speakers. Despite the progressing multilingual approaches, FND in low-resource languages remains obscure due to a lack of large-sized annotated corpora of real-world news. Large Language Models (LLMs) have emerged as a promising solution for multilingual FND. This study leverages the power of LLMs for an automated mechanism compared to traditional feature extraction methods. We curate the first large-sized multi-domain corpus, the Hook and Bait Urdu, with 78,409 fake and true news, and fine-tune the LLaMA 2 model for our proposed approach. We implement the curated dataset for two experiments. First, this study evaluates the dataset for unimodal text-based Urdu FND. The proposed LLaMA-based approach shows an accuracy of 0.978 and an F1-score of 0.971. For our second experiment, we fine-tuned the LLaMA 2-based framework for multilingual FND using the curated dataset (in Urdu) and the ISOT Fake News dataset (in English). Analytical and prediction performance comparisons with the previous studies validate the efficacy of the proposed framework with an accuracy of 0.984 and an F1-score of 0.980. The lightweight LoRA fine-tuning method, with  0.032% trainable parameters, ensured robust data handling, computational efficiency while leveraging early stopping and optimized hyperparameters for reliable and high-performing monolingual and multilingual FND. The real-world news dataset is publicly available for developing an automated FND mechanism to curb the threat of FN and related cybercrimes.

Article	Year
Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions. Sensors (Basel, Switzerland)\| PubMed ID: 36502215	2022
An SUI-based approach to explore visual search results cluster-graphs. PloS one\| PubMed ID: 36662776	2023
Digital society social interactions and trust analysis model. PeerJ. Computer science\| PubMed ID: 37346310	2022
Fingerprint image enhancement using multiple filters. PeerJ. Computer science\| PubMed ID: 37346560	2023
I-Cubid: a nonlinear cubic graph-based approach to visualize and in-depth browse Flickr image results. PeerJ. Computer science\| PubMed ID: 37705650	2023
BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization. PloS one\| PubMed ID: 37733686	2023
Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends. Journal of epidemiology and global health\| PubMed ID: 38353917	2024
DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets. Scientific reports\| PubMed ID: 38879689	2024
MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging. Journal of imaging\| PubMed ID: 39728219	2024
A two-phase transfer learning framework for gastrointestinal diseases classification. PeerJ. Computer science\| PubMed ID: 39896396	2024
Retraction Note: DNA encoding schemes herald a new age in cybersecurity for safeguarding digital assets. Scientific reports\| PubMed ID: 39934159	2025
Multi-domain Urdu fake news detection using pre-trained ensemble model. Scientific reports\| PubMed ID: 40082485	2025
GEAAD: generating evasive adversarial attacks against android malware defense. Scientific reports\| PubMed ID: 40195378	2025
An attention-enhanced few-shot model for event detection in online social networks. Scientific reports\| PubMed ID: 40295600	2025
Benchmarking Hook and Bait Urdu news dataset for domain-agnostic and multilingual fake news detection using large language models. Scientific reports\| PubMed ID: 40319160	2025

Naveed Ahmad

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