Despite current advances, cancer remains one of the biggest health challenges globally, and diagnosis must be made earlier to begin treatment. In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. I have achieved accurate results on more complex cancer datasets using advanced architectures of CNN + LSTM and attention-based models, along with the pre-trained models of VGG19, Xception, and AmoebaNet. Model reliability and interpretability are further improved using ensemble techniques such as confidence-based and XOR fusion. Experimental results in multiple multimodal datasets demonstrate the effectiveness of our hybrid approach by improving precision, recall, and F1 scores in various types of cancer. However, they have promising results and remain challenging to deploy for rare cancer subtypes or explain to gain clinical adoption. The proposed framework provides a basis for personalized cancer by developing machine learning innovations to advance precision medicine.

Fibrous composite materials are gaining popularity in various applications because of their exceptional attributes, such as high strength-to-weight ratio, high impact resistance, near-zero thermal expansion, and good corrosion resistance. These materials combine two or more fibrous materials with several physical and chemical properties to create a material with enhanced properties. The development of sustainable and environmentally friendly composite materials is increasing day by day to reduce environmental pollution and promote a more sustainable future. This research explores the physical and mechanical characteristics of cotton-glass, banana-glass, and jute-glass-reinforced epoxy composites, aiming to define their suitability for various applications. Tensile strength, flexural strength, and water absorption are the fundamental properties evaluated in this work. The hand lay-up technique was used to fabricate the composite, which involves manually layering the fiber and the matrix material. The study's findings provide significant insights into the potential application of composite materials in various industrial settings. Moreover, using sustainable and eco-friendly composite materials can help reduce environmental pollution. Although glass fiber is not biodegradable, it is easily recyclable. Other fibers used in this study are biodegradable, so it is a sustainable approach. In summary, studying the mechanical properties of composite materials provides valuable insights into their potential use in lightweight and durable diverse applications. Continued research may lead to more advanced composite materials with enhanced features for broader applications.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects movement and cognition, impacting millions of people worldwide. The diagnosis of PD primarily relies on clinical tests, which can often result in delayed identification of the disease. Recent advancements in data-driven methods using deep learning have demonstrated potential for improving early diagnosis by utilizing clinical and vocal inputs. This study conducted a comparative analysis of five deep learning models: Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Autoencoder, and Generative Adversarial Network (GAN), specifically for the detection of PD using vocal biomarkers. Among these models, the MLP achieved the highest predictive accuracy at 97.4 %. The RNN, GRU, and Autoencoder models attained a similar accuracy rate of 87.2 %. In contrast, the GAN model yielded an accuracy of only 76.9 %. The UCI vocal dataset from Kaggle was utilized in this research, along with extensive data preprocessing techniques to address missing values. Performance evaluation was conducted using multiple metrics. The results indicate that deep learning models can effectively diagnose PD using voice data, suggesting their potential to enhance diagnostic accuracy and support clinical decision-making. Furthermore, these models are feasible for large-scale integration into clinical workflows.