SNOW 3G is a synchronous, word-oriented stream cipher used by the 3GPP standards as a confidentiality and integrity algorithms. It is used as first set in long term evolution (LTE) and as a second set in universal mobile telecommunications system (UMTS) networks. The cipher uses 128-bit key and 128 bit IV to produce 32-bit ciphertext. The paper presents two techniques for performance enhancement. The first technique uses novel CLA architecture to minimize the propagation delay of the 2³² modulo adders. The second technique uses novel architecture for S-box to minimize the chip area. The presented work uses VHDL language for coding. The same is implemented on the FPGA device Virtex xc5vfx100e manufactured by Xilinx. The presented architecture achieved a maximum frequency of 254.9 MHz and throughput of 7.2235 Gbps.

Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.

The prime purpose of the proposed study is to construct a novel predictive scheme for assisting in the prognosis of criticality using the MIMIC-III dataset. With the adoption of various analytics and advanced computing in the healthcare system, there is an increasing trend toward developing an effective prognostication mechanism. Predictive-based modeling is the best alternative to work in this direction. This paper discusses various scientific contributions using desk research methodology towards the Medical Information Mart for Intensive Care (MIMIC-III). This open-access dataset is meant to help predict patient trajectories for various purposes ranging from mortality forecasting to treatment planning. With a dominant machine learning approach in this perspective, there is a need to discover the effectiveness of existing predictive methods. The resultant outcome of this paper offers an inclusive discussion about various available predictive schemes and clinical diagnoses using MIMIC-III in order to contribute toward better information associated with its strengths and weaknesses. Therefore, the paper provides a clear visualization of existing schemes for clinical diagnosis using a systematic review approach.

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