Android Controlled Smart Wheelchair for Disabilities

This paper describes a control technology of wheelchair which may feel more flexible than traditional joystick controlled one. The main objective of our research is to develop new control architecture for a motorized wheelchair as well as an embedded system for monitoring critical patients. Such a smart wheelchair is designed for the disabled people in the developing countries as it will be very low-cost than existing others. Controlling is possible by android operated mobile or tab. In addition to button control, motion sensor controlling mechanism also has implemented. Moreover, bio-metric features have made wheelchair more suitable for critical patients. If the patient is in hostile condition, the wheelchair will produce an alert by raising the alarm with the measurement of the heartbeat at a particular interval

Presenting features of locally advanced breast cancer : a crosssectional study

Worldwide, breast cancer is the most frequently diagnosed life-threatening cancer in women. This cross sectional study was done to assess the presenting features of locally advanced breast cancer. The mean age was 42.6 years with standard deviation 9.56, 86% were house wife, 53.5% were illiterate, 16% were postmenopausal and 75.8% had BMI of 20Kg/m2. Ninety one point five percent had menarche at or above 12 years of age and 26.6 % had history of using hormonal contraceptives. Tobacco user and positive family history were found in 21.2% and 5.35% of the cases. 84% were presented with only lump and 16% with both lump and ulceration. Right breast was involved in 51.1%. About 37% cases presented on 3rd month of their symptoms. Around 71.2% patients presented with lump >5cm in diameter, axillary lymph nodes were palpable in 81% and fixed in 31% of patients. Around Sixty three percent of patients were in stage IIIA. Still a large fraction of patients present with advanced stage with varied presentation, sometimes surgeons face difficulties to offer the treatment. Proper awareness, early presentation and early detection give them the opportunity to receive the best treatment. BSMMU J 2021; 14(4): 148-15

Molecular Subtyping: status of the molecular factors in the locally advanced breast cancer and its correlation with risk factors

Molecular factors play an important role in the management and treatment outcome of breast cancer. Molecular subtyping has been developed depending upon estrogen and progesterone receptors, human epidermal growth factor receptor-2 and ki76 level. This cross-sectional study was done to assess the molecular subtypes of locally advanced breast cancer and its associated risk factors. Total 94 patients with locally advanced breast cancer were included in the study. The mean age was 42.6 years with a standard deviation of 9.56. In total, 91.5 percent of respondents had menarche at or after the age of 12, and 26.6 % had used hormonal contra- ception in the past. Tobacco users and positive family history were found in 21.2% and 5.35% of the cases. Among the patients, 3.4 % of cases had their first childbirth after the age of 30 and 95.5% of patients feed their babies from their both breasts. Among 94 cases 5 did not have any child. Estrogen receptor was found positive in 35% of cases, progesterone receptor-positive patient was 33% and HER-2 was found positive in 39.4% of cases. Ki-67 level was found high in 66% of cases. Among the 94 cases, the Luminal A subtype was found in 18% and the Lumi- nal B subtype was found in 27.7% cases. The human epidermal growth factor receptor-2 subtype was found relatively less frequent than Luminal type B (24.5% vs. 27.7%). Triple-neg- ative breast cancer was most commonly diagnosed among the patients (almost 30%). The increased number of triple-negative variants signifies poor prognostic outcomes. The risk factor of breast cancer did not show any statistical correlation with molecular subtypes. BSMMU J 2021; 14(3): 57-6

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods