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Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle’s critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4–10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper.

The design of a power electronic interface for high voltage difference DC buses is a key aspect in DC microgrid applications. A multi-port non isolated interleaved high-voltage gain bidirectional converter, which facilitates bidirectional power transfer and islanded operation in a DC microgrid, is presented in this paper. The forward high-voltage transfer ratio is achieved using a voltage multiplier circuit, and the high-gain step-down power conversion is performed using a resonant power module. A novel power transfer selection algorithm is proposed to control power flow among the interfaces of the RES, ESS, and DC grid converters, which utilizes the net power difference as the basis for switching the converter. The proposed converter is simulated for a 24 V PV source, 12 V battery, and 400 V DC grid interface using MATLAB/SIMULINK. A 200 W hardware prototype is implemented. The simulation results for voltages, currents, and power flow among RES, ESS, and microgrid DC bus proved an excellent voltage regulation, efficient power conversion, and a feasible duty cycle range with high voltage gain. These observations are validated through equivalent experimental results. A comparison is made regarding achieved gain, component sizing, achievable power transfer modes, efficiency, and control complexity with existing converters for DC microgrid applications. The presented topology proved to be a better interface with multiple-mode support with high efficiency.

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.

HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network’s performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques.

Electricity theft presents significant challenges globally, with traditional detection methods often lagging behind sophisticated techniques. A misuse of authority can have several detrimental effects. These include rising energy consumption, strain on the infrastructure that supplies it, falling power company profits, and risks to public safety such as electrical shocks and fires caused by using electricity. The proposed model used an ensemble method involving voting and stacking methods to train a challenging imbalanced dataset of electricity theft. The ensemble method used logistic regression and random forest models with ADASYN (adaptive synthetic sampling) to achieve the best results. The dataset comprised 1034 customer records (2014–2016), exhibiting marked class imbalance that was corrected to equal class representation using ADASYN. On the ADASYN-balanced data, the stacking model (logistic regression + random forest) delivered class-wise precision/recall/F1 of 0.95/0.94/0.94 for “theft” and 0.94/0.95/0.94 for “non-theft,” with overall accuracy of 0.94. Discrimination performance was strong (ROC-AUC ≈ 0.94), surpassing the voting ensemble (AUC ≈ 0.93) when both were trained on balanced data. Confusion-matrix and metric profiles further show stacking on balanced data outperformed all imbalanced settings and the voting baseline. Experimental results showed that stacking with the combination of logistic regression and random forest achieved the best results from benchmarks of 94% accuracy, recall, and F1-score. These findings indicate a robust, lightweight approach for electricity theft detection that improves minority-class detection without sacrificing overall accuracy.

Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth modules and Arduino responsible for sending the sensor data to the game. Further, the sensor data is sent to a machine learning model, which detects the game played by the user. The detected game will be operated on human gestures. A publicly available dataset named IM-Sporting Behaviors is initially used, which utilizes triaxial accelerometers attached to the subject’s wrist, knee, and below neck regions to capture important aspects of human motion. The main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting activity. The proposed system uses artificial neural networks classifier giving an accuracy of 88.9%. The proposed system should apply to many systems such as construction, education, offices and the educational sector. Extensive experimentation proved the validity of the proposed system.

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding. Such scene-understanding task is a demanding part of several technologies, like augmented reality-based scene integration, robotic navigation, autonomous driving, and tourist guide. Incorporating visual information in contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which is usual in classical frameworks during scene understanding. In this paper, we propose a convolutional neural network (CNN) based segmentation method for the recognition of multiple objects in an image. Initially, after acquisition and preprocessing, the image is segmented by using CNN. Then, CNN features are extracted from these segmented objects, and discrete cosine transform (DCT) and discrete wavelet transform (DWT) features are computed. After the extraction of CNN features and computation of classical machine learning features, fusion is performed using a fusion technique. Then, to select the minimal set of features, genetic algorithm-based feature selection is used. In order to recognize and understand the multi-objects in the scene, a neuro-fuzzy approach is applied. Once objects in the scene are recognized, the relationship between these objects is examined by employing the object-to-object relation approach. Finally, a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image. The experimental results over complex scene datasets including SUN Red Green Blue-Depth (RGB-D) and Cityscapes’ demonstrated a remarkable performance.

In the new era of technology, daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds. To understand the scenes and activities from human life logs, human-object interaction (HOI) is important in terms of visual relationship detection and human pose estimation. Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained. Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures, occluded regions, and unsatisfactory detection of objects, especially small-sized objects. The existing HOI detection techniques are instance-centric (object-based) where interaction is predicted between all the pairs. Such estimation depends on appearance features and spatial information. Therefore, we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI. Furthermore, we detect the human body parts by using the Gaussian Matric Model (GMM) followed by object detection using YOLO. We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm. The interactions are linked with the human and object to predict the actions. The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.