Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous to the environment and improve the quality of life for the residents is one of the main goals of the new smart cities. In order to improve the reliability and sustainability of the transportation system, changes are being made to the way electric vehicles (EVs) are used. As EV use has increased, several problems have arisen, including the requirement to build a charging infrastructure, and forecast peak loads. Management must consider how challenging the situation is. There have been many original solutions to these problems. These heavily rely on automata models, machine learning, and the Internet of Things. Over time, there have been more EV drivers. Electric vehicle charging at a large scale negatively impacts the power grid. Transformers may face additional voltage fluctuations, power loss, and heat if already operating at full capacity. Without EV management, these challenges cannot be solved. A machine-learning (ML)-based charge management system considers conventional charging, rapid charging, and vehicle-to-grid (V2G) technologies while guiding electric cars (EVs) to charging stations. This operation reduces the expenses associated with charging, high voltages, load fluctuation, and power loss. The effectiveness of various machine learning (ML) approaches is evaluated and compared. These techniques include Deep Neural Networks (DNN), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) (DNN). According to the results, LSTM might be used to give EV control in certain circumstances. The LSTM model’s peak voltage, power losses, and voltage stability may all be improved by compressing the load curve. In addition, we keep our billing costs to a minimum, as well.

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models.

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.