Performance analysis of hybrid 5G cellular networks exploiting mmWave capabilities in suburban areas

Millimeter wave (mmWave) technology is considered as a key enabler for fifth generation (5G) networks to achieve higher data rates with low transmission power by offloading the users with low signal-to-noise-ratios. Millimeter wave networks operating at E and W frequency bands have available bandwidth of 1 GHz or more to provide higher data rates whereas their propagation characteristics differ greatly from the conventional Ultra High Frequency (UHF) networks operating at sub 6 GHz frequency band. The purpose of this paper is to investigate the performance in terms of coverage and rate, of hybrid cellular networks where base stations (BSs) operating at mmWave and sub 6 GHz bands coexist in suburban environment such as a university campus. The actual building locations within a suburban university campus are modeled as blockages and the analysis is carried out for different densities of UHF and mmWave BSs for different densities of outdoor users. Our analysis also highlight the fact that mmWave cellular networks are predominantly noise-limited due to larger available bandwidth in comparison to the interference limited conventional UHF networks. Extensive simulation results demonstrate the effectiveness of dense deployment of mmWave BSs to achieve better coverage and rate probabilities in comparison to the stand alone UHF network

Anaesthetic Practices and Maternal Outcome in Rising Placenta Accreta Spectrum in Tertiary Care Hospital

Objective: This study was carried out to determine whether the rate of abnormal placentation is increasing in concurrence with the cesarean section and to assess risk factors and outcomes with multidisciplinary team interventions and anesthetic practices. Study design: Prospective cohort study. Material & Methods: A study was conducted in the department of anaesthesia from January 2014 to December 2017. All candidates under the spectrum of placenta accreta were observed for maternal age, parity, mode of anesthesia, blood loss, and outcome. Results: Out of 109 patients, the preoperative diagnosis of PAS was made up of 100 (91.74%) and intraoperative diagnosis of 9 (08. 26%) patients. According to the mode of anesthesia, 100 (91.74%) patients received GA, and 09 (08.26%) patients received spinal anesthesia. In 06 (05.49%) patients, spinal was converted to GA. Perioperative CPR was done in 05 (04.58%) cases. Out of 109 cases, 83 survived uneventfully, and 21 developed complications. 05 patients expired in the following days. (01 immediately postoperative period, 02 in 1st 24 hours and 02 in 1st 48 hours. Conclusion The rate of placenta accreta increased in conjunction with cesarean deliveries; the most important risk factors were previous cesarean delivery, placenta previa, and advanced maternal age and outcomes improved in a multidisciplinary team intervention

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art

A Deep Learning-based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art