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Background Internet dependency behavior was found to be prevalent among adolescents even before the first wave of COVID-19 lockdowns across the world including India. Adolescent users develop Internet addiction due to various risk factors. Aim The aim is to measure the prevalence and psychosocial predictors of internet addiction among adolescents before the first wave of the COVID-19 lockdown in India. Methods A cross-sectional, descriptive study before the first wave of the COVID-19 lockdown, included 1199 adolescents of both genders, aged 11 to 19 years, at selected educational settings from a city in south India, by using Young's Internet addiction test (IAT)-20 and structured questioner. Results The study found almost all the participants (100%) were using the internet in a day and the highest number of subjects started using the internet during their 6 standard of education (13%). Before the first wave of COVID-19 lockdown, the prevalence of a total of mild, moderate, and severe forms of internet addiction among adolescents was 65%. Individual, family, and community-related risk factors were found significant association with Internet addiction. The age of 14-16 years (OR 2.050, p= 0.000), duration of internet use in a day (OR 0.740, p= 0.064), financial matters (OR 0.981, p=0.016), total internet addiction score (OR 1.03, p=0.035) and timings of internet use (OR 1.161, p=0.004), were significant predictors of Internet addiction. Conclusion Internet addiction was prevalent and a notable behavior addiction among adolescents during the margin time of pre-pandemic and the first wave of the COVID-19 lockdown in India. The study highlighted many significant psychosocial risk factors and predictors of Internet Addiction in adolescents, thus the need for a panoramic approach to identify internet addiction in adolescents, to bring the modest behavior of healthy use of the internet in adolescents.

The focus on power optimization in embedded systems is especially important for embedded applications since it has brought in many methods and factors that are necessary for developing systems that are both power- and area-efficient. In contrast to the current delayed wavelet μ-law proportionate least mean square (DWMPLMS) and delayed least mean square (DLMS) algorithms, this work offers the development of adaptive filters based on the least mean square (LMS) method, which improves power and timing performance. In order to improve area and time efficiency, the proportionate least mean square (PLMS) algorithm's architecture has been modified to remove delay, add a proportionate gain block, design for a fixed length, include an approximate multiplier block, and swap out standard blocks for floating-point adder and divider blocks. According to a power and temporal comparison with the DWMPLMS and DLMS algorithms, field-programmable gate array (FPGA) synthesis reduces power usage by 95% for a 32-bit filter length in PLMS when compared to the above methods.

Diagnosis of Epilepsy is immensely important but challenging process, especially while using traditional manual seizure detection methods with the help of neurologists or brain experts' guidance which are time consuming. Thus, an automated classification method is require to quickly detect seizures and non-seizures. Therefore, a machine learning algorithm based on a modified XGboost classifier model is employed to detect seizures quickly and improve classification accuracy. A focal loss function is employed with traditional XGboost classifier model to minimize mismatch of training and testing samples and enhance efficiency of the classification model. Here, CHB-MIT SCALP Electroencephalography (EEG) dataset is utilized to test the proposed classification model. Here, data gathered for all 24 patients from CHB-MIT Database is used to analyze the performance of proposed classification model. Here, 2-class-seizure experimental results of proposed classification model are compared against several state-of-art-seizure classification models. Here, cross validation experiments determine nature of 2-class-seizure as the prediction is seizure or non-seizure. The metrics results for average sensitivity and average specificity are nearly 100%. The proposed model achieves improvement in terms of average sensitivity against the best traditional method as 0.05% and for average specificity as 1%. The proposed modified XGBoost classifier model outperforms all the state-of-art-seizure detection techniques in terms of average sensitivity, average specificity.