Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Coronary arterial bypass graft patency evaluated by multi-detector computed tomography and magnetic resonance imaging

The progression of atherosclerosis of the coronary artery does not stop after coronary arterial bypass grafting (CABG) surgery.……

Digital Beamforming by Multiple Chirp Zeta Transforms for 3-D Sonar Imaging Systems

The computational load is prohibitive for real-time image generation in 3-D sonar systems, particularly when the steering angle approximation is required. In this paper, a novel multiple Chirp Zeta Transforms （MCZT） beamforming method in frequency domain is being proposed. The single long-length Chirp Zeta Transform （CZT） in the original CZT beamforming is replaced by several CZTs with smaller lengths for different partitions along each dimension. The implementing routine of the algorithm is also optimized. Furthermore, an avenue to evaluate the estimating error for the angle approximation in 3-D imaging applications is presented, and an approach to attain valid partitions for the steering angles is also flhistrated. This paper demonstrates a few advantages of the proposed frequency-domain beamforming method over existing methods in terms of the computatianal complexity.

The association of the uterine motion with bladder volume during radiotherapy in gynecological malignancies

Aim:This study was performed to assess the extent of interfraction uterine motion during radiotherapy for cervical cancer and uterine body carcinoma while maintaining a strict bladder filling protocol.Methods:Twenty-four patients with cervical cancer or uterine body carcinoma who were treated on a linear accelerator,were recruited.During the course of external beam radiotherapy,cone beam computed tomographic scans were taken,once at the start of treatment and then weekly until the completion of the radiotherapy course.Patients were instructed to maintain a strict bladder filling protocol.After negating the effect of patient’s setup error by offline cone beam computed tomographic imaging,the position of the uterus was defined in the clinical target volume.Then the position of the uterus was compared in the following weekly scans.The position of the uterus was also correlated with the position and the filling of the bladder.This change in uterus position was measured separately in the anterioposterior(AP),superioinferior(SI),and lateral directions.Results:According to calculations based on weekly imaging,The mean values of shift in AP,SI,and lateral directions were respectively 0.67,0.29,and 0.23 The mean extent of motion in the uterine position on a daily basis for individual patients ranged from-2.28 to+1.3 in AP,-0.56 to+0.71 in SI,and from-0.6 to+0.45 in lateral directions.Conclusion:At least once a week cone beam computed tomography might be necessary to minimize the geometrical miss and deliver the planned doses to the target tissue and normal structure provide best results with minimum toxicity by maintaining a bladder volume of about 100 mL and an empty rectum during the whole course of treatment.The daily anatomical shift and contour of the patients maintaining a bladder volume of approximately 100 mL with an empty rectum may result in asymmetrical conforming to the planning target volume and hence appropriate and adequate planning target volume margins are required.