Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

Rainwater is a free and renewable source used for domestic purposes but due to storage constraints, 36% of rain water is used in India even though heavy rainfall occurs during monsoon months. Nowadays the roads and their development in city environments are incalculable. The outstanding backlog is that most of the time, the drainage is filled by rain that falls on roads lead to less possibility of rain water usage. This work describes of saving rain water via external sensor -equipped pipes attached to the roadside. Whenever rain water fall on sensors it will be sensed, so that the pipes will open and rain water will fall through the fitted pipes and further it is transferred to storage tank with the aid of servo motors and DC motors. Through this prototype construction the rain water quantity of 55% is getting saved and also supports for ground water recharge

Wireless sensor networks (WSNs) are becoming more valuable in environmental monitoring and industrial automation. Many WSN applications need high-performance adders and multipliers for efficient computation. Due to power efficiency, latency reduction...