Cancer of bone marrow is classified as Acute Lymphoblastic Leukemia (ALL), an abnormal growth of lymphoid progenitor cells. It affects both children and adults and is the most predominant form of infantile cancer. Currently, there has been significant growth in the identification and therapy of acute lymphoblastic leukemia. Therefore, a method is required that is capable to accurately assessing risk by an appropriate treatment strategy that takes into account all relevant clinical, morphological, cytogenetic, and molecular aspects. However, to enhance survival and quality of life for those afflicted by this aggressive haematological malignancy, more research and clinical trials are required to address the issues associated with resistance, relapse, and long-term toxicity. Consequently, a deep optimized Convolutional Neural Network (CNN) has been proposed for the early diagnosis and detection of ALL. The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. The proposed deep optimized CNN model is tuned using the hyperparameters such as 30 epochs, batch size 32 and optimizers, namely Adam and Adamax. Out of the two optimizers, the proposed deep optimized CNN model has outperformed using Adam optimizer with the points of accuracy and precision as 0.96 and 0.95, respectively.

In this research work, a soft computing optimization operating approach is developed for a multi-objective aspirational level fractional transportation problem. In the proposed technique, a mathematical model is formulated for the multi-objective aspirational level fractional transportation problem (MOFTP) based on the highest value of one and all objectives of the model. We also used the symmetry concept over our model to identify the best optimum solution based on symmetrical data. We constructed the membership grades for the set of fetched parameters having symmetry. In this work, we also used the concept of ranking function in our mathematical model to obtain the optimum solution of the fuzzy multi-objective fractional transportation. In this proposed algorithm, the aspiration levels are also associated with the objective function of MOFTP. We are also proposing a new approach for the optimization of fractional problems in which the objectives are being optimized by using the numerator function and denominator function simultaneously. Further, a methodology is also developed to find the average cost for each fractional objective of the model. After that, we will find the ranking function for each parameter by using the defuzzification method. By this methodology, we will be able to convert the MOFTP into a bi-objective transportation problem. The provided technique is elaborated with the help of numerical computations to prove the beauty and power of the proposed technique.

The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.

Energy-harvesting wireless sensor networks (EH-WSNs) require clustering with precise sensing-awareness and awareness in harvested-energy dynamics and communication costs. This paper introduces MCSOC (Modified Cat-Swarm-Optimization based clustering), an approach with a domain-aware, multi-objective fitness for the choice of cluster-heads that co-optimizes: (i) residual energy and (ii) intra-cluster length; and environmental aware optimization in terms of (iii) inter-cluster transmission cost to the sink, and finally, (iv) distances between EH nodes and the sink. In a first-order radio energy model, with static nodes and a single central sink, MCSOC is evaluated on two deployments (200 × 200 m2 and 500 × 500 m2, respectively, with 200 nodes overall) over an average of 30 runs. We compare with NEHCP, ROTEE, SMEOR, and GAPSO-H on lifetime, throughput, residual energy, and stability. Results demonstrate that our MCSOC achieves longer network lifetime, high throughput, and a higher saving proportion of early dead nodes compared with benchmark methods that consider energy harvesting. As a result, MCSOC over GAPSO-H and SMEOR method, simulation results indicate that MCSOC enhances network performance, stability, and throughput by 42.13%, 45.57%, and 48.48% and 63.13%, 62.2%, and 58.68% respectively. These properties enable MCSOC to be used as a practical long-lifetime sensing in precision agriculture, smart-city environmental monitoring, and industrial health deployment scenarios.

Handwritten signature verification is one of the frequently used biometric in administrative, financial, legal, and similar scenarios to verify the identity of a person. Unfortunately, the offline nature of this task makes it more challenging as it involves intra-signature variance, and various temporal and environmental factors. This paper presents a novel offline signature verification system (OSVS) called SignGuard. It is designed by using Gray Wolf Optimization (GWO) for preprocessing. Also, Principal Orientation Alignment (POA) is used to mitigate their rotation as the proposed method has rotation sensitive descriptors. It is followed by two writer-independent models with new texture features namely, Centre Symmetric local binary pattern (CS-LBP) and Orthogonal Central Symmetric Local Binary Pattern (OC-CSLBP). They are trained using hybrid machine learning framework such that Support Vector Machine and XGBoost classifiers are integrated for the verification of a signature image in an offline mode. The performance of SignGuard is exploited on CEDAR, SID, and BHSig260 datasets, along with a novel dataset called DeepSignVault, such that OC-CSLBP and CS-LBP yields an accuracy of 98.77% and 97.46%, respectively. It is observed that SignGuard outperforms the existing OSVSs. The proposed architecture shows enhanced security and reliability for real-world applications in business, legal, and administrative systems. The outstanding performance of SignGuard highlights the authenticity of legal documents and financial transactions.

The proposed multi-modal deep learning system for lung cancer diagnosis and characterisation uses structural (CT), functional (PET), and clinical (EHR) data. The heterogeneous information fusion technique uses a clinical data encoder, an attention-based fusion mechanism, and a convolutional neural network backbone for image feature extraction. Multi-task learning combines tumor categorization and characterization. Optimal hyperparameters for model training are 0.001 learning rate, 32 batch size, and 0.3 dropout rate for steady convergence and enhanced generalization. The ablation investigation’s 9% accuracy gain over the baseline model is mostly due to the attention mechanism. As model components are added, the ablation research shows a continuous and considerable performance increase. From the basic setup, adding PET data, EHR-based clinical characteristics, and enhanced fusion techniques improves all assessment measures. Attention-based fusion improves the most by adaptively weighting modalities. Compared with the baseline CT-only CNN model, the proposed attention-guided multimodal framework achieved consistent improvements across all evaluation metrics. Specifically, classification accuracy increased from 88.2% to 96.5%, corresponding to an absolute improvement of 8.3 percentage points. Precision improved from 87.5% to 95.8% (+ 8.3 percentage points), recall increased from 86.9% to 96.2% (+ 9.3 percentage points), and the F1-score increased from 87.2% to 96.0% (+ 8.8 percentage points). Similarly, the AUC-ROC improved from 0.901 to 0.982, representing an absolute increase of 0.081. These results demonstrate that the proposed multimodal attention-based framework substantially enhances classification performance and discrimination capability compared with the baseline model. According to five-fold cross-validation, the proposed model achieved an average accuracy of 96.5 ± 0.23%, precision of 95.8 ± 0.26%, recall of 96.2 ± 0.21%, F1-score of 96.0 ± 0.24%, and AUC of 0.982 ± 0.003. The performance gains were statistically significant (p < 0.05) compared with competing configurations and baseline models, confirming the robustness and effectiveness of the proposed framework.