International Journal of Interpreting Enigma Engineers (IJIEE)

A Dense Feature Fusion Framework for Multi-Modal Neuroimaging in Alzheimer’s and Parkinson’s Disease

Srinidhi Samboju, Vallakavi Praneetha, Bathka Sowmya — Sat, 27 Sep 2025 00:00:00 +0000

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) present significant obstacles in early diagnosis due to the complex interplay of structural and functional biomarkers. Multi-modal neuroimaging provides complementary information, yet integrating heterogeneous features remains a persistent challenge. In this work, we propose Dense Feature Fusion Network (DFF-Net), an end-to-end deep learning framework that leverages MRI and PET modalities through a dense feature fusion block and cross-modal attention mechanism. Our approach facilitates richer representation learning by preserving both modality-specific and shared features. We evaluate DFF-Net on benchmark datasets such as ADNI and PPMI, achieving superior performance over baseline fusion strategies in terms of accuracy, AUC, and F1-score. Furthermore, an interpretability analysis through attention maps pinpoints critical brain regions involved in neurodegenerative progression. The proposed model demonstrates the strong potential of dense feature fusion in improving clinical decision-making for early and accurate detection of neurodegenerative disorders.

SMART ARDUINO-BASED SOLAR TRACKING SYSTEM FOR ENHANCED PV EFFICIENCY AND SUSTAINABLE POWER GENERATION

B.Vijayalaxmi — Sat, 27 Sep 2025 00:00:00 +0000

With rising global energy demands and heightened environmental concerns, renewable energy has emerged as crucially significant alternative energy sources in contrast to the conventional power plants. The sector of renewable energy is one of the key industries of growth for most countries due to its eco-friendly and financial benefits. Out of all renewable resources, solar power is perhaps the most valuable resource, particularly for rural areas where access to the conventional sources of power could be weak.

The aim of this project is to create a real-time solar tracking system using an Arduino Uno for the purpose of attaining the highest energy harvesting. The system tries to make efficiency optimal by managing the position of the solar panel, which moves dynamically according to the movement of the sun. The project has two broad stages: hardware development and software development. In the hardware component, two light-dependent resistors (LDRs) are used to detect maximum light intensity and position the solar panel to receive maximum energy. A servo motor, as instructed by the Arduino, places the panel according to LDR input. The system efficiency has been tested and compared with a stationary solar panel to find out its performance.

This project provides a cost-effective way of solar tracking, which assists in improving energy efficiency and sustainability in solar power generation.

CROP YIELD PREDICTION AND FERTILIZATION OPTIMIZATION USING MACHINE LEARNING

Purvi Lakhotia, Keshav Kant, K.Uday, M.Yoshitha, V.Varshitaa, S. Mothi Sree — Sat, 27 Sep 2025 00:00:00 +0000

Crop selection presents a number of complex issues for the agricultural industry, requiring an informed and flexible strategy for the best possible production and resource management. Farmers struggle with the complex variety of soil properties, the ever-changing dynamics of weather patterns, and the never-ending quest to maximize crop yield while consuming the fewest resources possible. Without data-driven solutions, conventional approaches or trial-and-error techniques frequently result in less-than-ideal crop selections, lower yields, and financial losses for farmers. To develop a platform that can read and analyze a variety of environmental parameters, the suggested solution promotes the fusion of state-of-the-art technology and data analytics. This approach aims to produce customized suggestions for farmers by combining soil analysis data, weather forecasting algorithms, and historical crop performance. With the help of these suggestions, they should be able to confidently make strategic planning decisions that are in line with the complex needs of their unique agricultural environments. In conclusion, the suggested solution promotes a creative strategy that uses data analytics and technology breakthroughs to provide farmers with precise, tailored, and flexible crop suggestions. This solution seeks to transform agricultural decision-making processes by providing farmers with practical insights, promoting increased productivity, resource efficiency, and long-term farming profitability.

A MULTI-SCALE DENSE CONVOLUTIONAL FRAMEWORK FOR ECG SIGNAL ENHANCEMENT IN NON-GAUSSIAN NOISE ENVIRONMENTS

Sat, 27 Sep 2025 00:00:00 +0000

The electrocardiogram (ECG) signal suffers greatly due to various non-Gaussian noise sources, such as baseline wander, muscle artifact, and impulsive-related signal disruptions due to electrode movement. More conservative denoising algorithms, like adaptive filtering, wavelet transforms or empirical mode decomposition, are weak in preserving the fine morphology of ECG waveforms in such cases, and tend to distort diagnostically meaningful ECG waveform components (such as the QRS complex and P-T waves). As the need to deploy robust and high-fidelity noise suppression has grown in clinical and wearable healthcare settings, more sophisticated noise suppression techniques have become necessary.

To solve this problem, we introduce an Advanced Dense Convolutional Network (ADCD-Net) to denoise ECGs in non-Gaussian noise conditions. It is an encoder-decoder design and a broad band of connections that can effectively go through noise cancellation and contains useful cardiac changes. A hybrid loss of time domain reconstruction, spectral similarity are used to train the network to incorporate identity artifact at both the impulsive and broadband scales. Benchmark ECG experimental assessments show that ADCD-Net outperforms classical and more recent deep learning techniques and yields increasing SNR, PRD, and QRS detection performance. It will provide a valid and highly accessible solution on real-time monitoring of ECG on the telemedicine and wearable health care system.

CNN–LSTM HYBRID ARCHITECTURE FOR ROBUST BRAIN TUMOR DETECTION AND ANALYSIS

Sat, 27 Sep 2025 00:00:00 +0000

Timely and proper diagnosis of brain tumor is essential at an early stage to enhance patient survival and treatment strategies. The application of deep learning to medical imaging has been quite promising, but most of the existing methods use exclusively convolutional networks that do not exploit time in sequential imaging data and therefore do not consider temporal relationships in medical images. The proposed method CNNLSTM hybrid architecture that combines extraction of spatial features and sequence learning to effectively model brain tumors and analyze them out of MRI scans. The CNN component effectively captures high-level spatial patterns in tumor regions whereas the LSTM network captures sequential dependencies across slices to model contextual patterns. The suggested approach is compared with four publicly available baseline methods: VGG16, ResNet50, DenseNet121, and InceptionV3 on a publicly available brain MRI dataset containing glioma, meningioma, pituitary tumor, and normal samples. Experimental findings have shown that CNNLSTM model performs better with 97.8, 97.4, 98.1, and 97.7 accuracy, precision, recall, and F1-score respectively, gaining better results than VGG16 (92.3%), ResNet50 (94.5%), and DenseNet121 (95.8) and InceptionV3 (94.9). These findings underscore the merit of using both convolutional and recurrent networks to encapsulate both spatial and sequential features, thus facilitating more accurate brain tumor diagnosis.