Automation in every part of life has become a frequent situation because of the rapid advancement of technology, mostly driven by AI technology, and has helped facilitate improved decision‐making. Machine learning and the deep learning subset of AI provide machines with the capacity to make judgments on their own through a continuous learning process from vast amounts of data. To decrease human mistakes while making critical choices and to improve knowledge of the game, AI‐based technologies are now being implemented in numerous sports, including cricket, football, basketball, and others. Out of the most globally popular games in the world, cricket has a stronghold on the hearts of its fans. A broad range of technologies are being discovered and employed in cricket by the grace of AI to make fair choices as a method of helping on‐field umpires because cricket is an unpredictable game, anything may happen in an instant, and a bad judgment can dramatically shift the game. Hence, a smart system can end the controversy caused just because of this error and create a healthy playing environment. Regarding this problem, our proposed framework successfully provides an automatic no‐ball detection with 0.98 accuracy which incorporates data collection, processing, augmentation, enhancement, modeling, and evaluation. This study starts with collecting data and later keeps only the main portion of bowlers’ end by cropping it. Then, image enhancement technique are implied to make the image data more clear and noise free. After applying the image processing technique, we finally trained and tested the optimized CNN. Furthermore, we have increased the accuracy by using several modified pretrained model. Here, in this study, VGG16 and VGG19 achieved 0.98 accuracy and we considered VGG16 as the proposed model as it outperformed considering recall value.

An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Generally, the white and red water lily cultivars' stems are consumed as vegetables in Bangladesh. However, data on the nutritional benefits and antioxidant properties of these aquatic plant’s stem are scant. Considering the aforementioned possibilities, the goal of this study was to evaluate the nutritional value, anti-nutritional factors, and antioxidant activity of two common Nymphaea species such as Nymphaea nouchali (white variety), and Nymphaea rubra (red variety) grown in Bangladesh. The proximate analysis and mineral content were determined by following AOAC methods. Crude fiber content was significantly higher in the white lily (19.56±0.68%) compared to the red cultivar (16.24±1.05%). Conversely, the red lily showed a higher value (18.52±1.01%) for ash content than the white species (15.44±0.79%). However, the varieties had no significant differences in moisture, protein, fat, and carbohydrate content. Both stems were rich in essential minerals, whereas Na and Cu were significantly higher in white water lily (WWL); however, the red water lily (RWL) stem possessed significantly copious amounts of K and P. On the other hand, there was no significant difference between two species in the case of Ca, Mg, Fe, and Zn content. The assay showed that the total phenolic contents (mg GAE/g) and total flavonoid contents (mg QE/g) were 16.51±0.05 and 15.48±02, 476±0.1 and 7.846±0.1 in white and red water lily stems respectively. In addition, the anti-nutrient screening revealed that both species were positive for saponin and tannin. Furthermore, the extract of RWL stem showed more antioxidant activity (IC50 value 28.48±0.12µg/ml) against DPPH than WWL (IC50 value 36.67±0.09 µg/ml). Thus, water lily stems can be incorporated into our diet as a potential natural source of antioxidants and vital elements.

Several Ficus species showed potential antimicrobial, antidiabetic, antioxidant, wound healing, anti-inflammatory, anticarcinogenic, antidiarrheal, and hepatoprotective properties. Hence, this study examined the nutrachemical composition, antioxidant, and antimicrobial activity of two commonly consumed Ficus fruits (Ficus racemosa and Ficus hispida), from southeast Bangladesh, considering the growing demand for natural food supplements and preservatives. Proximate analysis revealed that F. hispida had significantly higher moisture (85.73±0.75%), crude fiber (26.53±0.15%), and inorganic content (8.87±0.10%). Conversely, F. racemosa contained higher fat (9.30±0.02%) and carbohydrate (48.57±0.13%) levels, while protein content was similar between the two species. Mineral analysis revealed that F. racemosa was rich in sodium, potassium and copper whereas F. hispida had higher calcium, magnesium, phosphorus and iron; zinc content was comparable in both species. Phytochemical screening indicated the presence of tannins and alkaloids in both fruits, though saponins were highly present in F. racemosa but absent in F. hispida. The total phenol content (mg GAE/g) and flavonoid content (mg QE/g) were notably higher in F. racemosa (10.42±0.03 and 20.05±0.15, respectively) compared with F. hispida (5.74±0.06 and 15.97±0.75, respectively). Antioxidant capacity, measured by DPPH scavenging activity, was significantly stronger in F. racemosa (IC₅₀ = 16.80±1.08) than in F. hispida (IC₅₀ = 35.83±1.75). Multivariate analysis revealed a distinct separation between the two species based on principal components, highlighting F. racemosa as rich in antioxidants, while F. hispida had higher mineral content. However, extracts from both fruits showed no inhibition zones against foodborne pathogens, Staphylococcus aureus and Escherichia coli. These findings suggested that Ficus fruits may serve as natural sources of essential nutrients to address malnutrition and oxidative stress-related diseases.