Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Breast cancer (BC) has recently been a major issue. Although numerous BC detection methods are used in medical image processing, the correct detection and classification of benign and malignant micro-calcifications is problematic. This research provides an effective BC detection and classification method using AG2ConvNet and Adap-BI magnification. The input image is first obtained from BUSI and MIAS datasets. Image type conversion improves categorisation after capture. Next, data augmentation uses rotation and flipping. Data is pre-processed using sigmoid scaling and Gaussian filter (GF). Adap-BI magnifies pre-processed data, and the minimisation principle-adapted Canny edge detector detects edges. The AG2ConvNet classifier uses the edge-detected output to diagnose normal, benign, and malignant tumours from the digital mammography picture. Finally, the outbreak prediction models results are compared to existing methods to verify its efficacy. Results showed that the proposed method outperformed baseline methods. The suggested model outperforms EL-CNN, TL-DRS, and AM-SFF approaches with 95.41% accuracy. This shows the model's superiority and importance in improving breast cancer detection and classification, making it a potential medical image processing tool.

Social connections developed within narrow cultural limits, such as physical locations, prior to the invention of information and communication technology (ICT). Social technologies have revolutionised online social networks, user-generated content, and rich human behaviour data. Online social networks (OSN) promote social interaction but also trolling, hate speech, and cyberbullying. NLP-based automatic detection is essential to ending cyberbullying. A deep learning algorithm is suggested to detect cyberbullying aggression in this work automatically. Pre-processing, feature extraction, feature selection, and classification are among the processes included in the suggested workflow. The initial pre-processing steps for the Twitter database include noise removal, tokenisation, and stemming. The features from the pre-processed database have been extracted using the SAE, TF-IDF, and other techniques. To choose the subset of characteristics, the SCOA is next applied. FWLSTM classifier is then given features. The K-nearest neighbour (KNN), ANN, random forest (RF), and EK-SVM classifiers are contrasted with the FWLSTM classifier. Results are evaluated using precision, recall (sensitivity), specificity, false positive, false discovery, miss, and accuracy.

Dental X-ray picture segmentation assists tooth diagnosis. Too much chocolate and unhealthy diets have increased tooth diseases in recent decades. Dental radiography supports clinical diagnosis, treatment, and quality assessment. Clinical quality has been improved by digitalising dental X-ray image analysis systems. This inspires an early-detection dental disease prediction model. Dental X-ray image segmentation for disease diagnosis is gaining attention. Deep learning has grown in various image processing domains. Image segmentation is a key field of computer vision research. The U-Net, a popular picture segmentation method, has been widely applied to medicine. Here, a unique deep learning system, the UNet model improved with ANN, notably MLP, segments dental pictures better. An ANN-enhanced U-Net split the X-ray pictures, and a GLCM selected the features. CNN-mobile net classifier. Four metrics - global accuracy, mean accuracy, mean IoU, weighted IoU, and MeanBFScore - and classification with precision, recall, Fscore, specificity, and accuracy evaluate segmentation methodology. Our method is accurate and outperforms others in all segmentation and classification tasks.

Accurate water leak localisation remains a significant challenge in pipeline maintenance, water distribution networks, and building water systems. Traditional methods using pressure or acoustic monitoring often lack precision, leading to wasted water and infrastructure damage. This paper proposes a novel methodology to enhance leak localisation accuracy by combining machine learning and physical modelling. The strengths of two techniques are leveraged: advection-diffusion models predict the spread of a leak over time based on the movement of water in a pipeline or a building's water system, and autoencoders, a type of neural network, learn encoded representations of the current sensor data under normal operating conditions along with common adversarial patterns. In the proposed work, autoencoders are combined with advection-diffusion models to achieve significantly improved performance in water leak localisation. Experiments underscore that this combined approach can help reduce false alarms in locating water leaks, preventing further damage and reducing water waste. The proposed model shows improved performance in terms of precision, recall, and accuracy over existing systems. The improved performance is due to the effective hybridisation of physical and neural network models for the reduction of false alarm rates in water leak localisation.

The challenge of sarcasm detection in social media is heightened by its informal vernacular, the necessity for context, and the nuanced linguistic indicators that define sarcasm. This study explores the effectiveness of bi-LSTM models in conjunction with GLOVE and word2vec embeddings in identifying sarcastic expressions within social media platforms. This empirical evidence suggests that bi-LSTM models equipped with GLOVE embeddings surpass those with word2vec embeddings in all performance metrics, including accuracy, precision, recall, and F1-score. The bi-LSTM models demonstrate an improved ability to discern sarcasm's subtleties in text thanks to GLOVE's ability to encapsulate semantic meanings and contextual nuances. This research underscores the critical role of advanced pre-trained embeddings like GLOVE in elevating NLP tools for sarcasm detection on social media. Such advancements are particularly relevant to fields that depend on precise interpretation of user sentiment, such as brand monitoring, sentiment analysis, and broader social media analytics.

Facial expression recognition (FER) is crucial for applications in human-computer interaction, security, and healthcare. Traditional methods often struggle with issues such as limited generalisation, high computational demands, and handling complex emotions. To address these challenges, the research offers a novel methodology that combines advanced image pre-processing, feature extraction, and classification techniques. This initiates with image pre-processing that standardises facial images by reducing noise. Then the feature extraction employs a shallow convolutional neural network (CNN) for texture-based features. Then, the Softmax classifier for emotion prediction and incorporates hyperparameter tuning using the equilibrium optimiser (EO) algorithm. Additionally, feature selection is optimised using the improved cat swarm optimisation (ICSO) algorithm to refine the feature set. The integration of an ensemble classifier combining multi-layer perceptrons (MLPs) and ordinal logistic regression improves the robustness of the model, utilising soft labels. Experimental results demonstrate significant improvements over existing methods and achieve an accuracy of 99.12%.