ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic Seizures

We proposed an Attentive Recurrent Neural Network (ARNN), which recurrently applies attention layers along a sequence and has linear complexity with respect to the sequence length. The proposed model operates on multi-channel EEG signals rather than single channel signals and leverages parallel computation. In this cell, the attention layer is a computational unit that efficiently applies self-attention and cross-attention mechanisms to compute a recurrent function over a wide number of state vectors and input signals. Our architecture is inspired in part by the attention layer and long short-term memory (LSTM) cells, and it uses long-short style gates, but it scales this typical cell up by several orders to parallelize for multi-channel EEG signals. It inherits the advantages of attention layers and LSTM gate while avoiding their respective drawbacks. We evaluated the model effectiveness through extensive experiments with heterogeneous datasets, including the CHB-MIT and UPenn and Mayos Clinic, CHB-MIT datasets. The empirical findings suggest that the ARNN model outperforms baseline methods such as LSTM, Vision Transformer (ViT), Compact Convolution Transformer (CCT), and R-Transformer (RT), showcasing superior performance and faster processing capabilities across a wide range of tasks. The code has been made publicly accessible at \url{https://github.com/Salim-Lysiun/ARNN}.Comment: 9 pages, 7 figures, Journal Pape

Performance of Functionally Graded Exponential Annular Fins of Constant Weight

The present work aims at investigating the performance of exponential annular fins of constant weight made of functionally graded materials (FGM). The work involves computation of efficiency and effectiveness of such fins and compares the fin performances for different exponential profiles and grading parameters, keeping the weight of the fin constant. The functional grading of thermal conductivity is assumed to be a power function of radial co-ordinate which consists of parameters, namely grading parameters, varying which different grading combinations can be investigated. Fin material density is assumed to be constant and temperature gradient exists only along the radial direction. The convective coefficient between the fin surface and the environment is also assumed to be constant. A general second-order governing differential equation has been derived for all the profiles and material grading. The efficiency and effectiveness of the annular fin of different geometry and grading combinations have been calculated and plotted and the results reveal the dependence of thermal behavior on geometry and grading parameter. The effect of variation of grading parameters on fin efficiency and effectiveness is reported. The results are provided in the form of 2-D graphs, which can be used as design monograms for further use

Solution of Travelling Salesman Problem based on Metaheuristic Techniques

The traveling salesman problem is a classic problem in combinatorial optimization. This problem is to find the shortest path that a salesman should take to traverse through a list of cities and return to the origin city. The list of cities and the distance between each pair are provided. It is an NP-complete problem i.e., class of computational problem for which no efficient solution algorithm has been found, presently there is no polynomial solution available. In this paper, we try to solve this very hard problem using various heuristics such as Simulated Annealing, Genetic Algorithm to find a near-optimal solu-tion as fast as possible. We try to escape the local optimum, using these advanced heu-ristic techniques

Comparative Study of Artificial Neural Network based Classification for Liver Patient

The extensive accessibility of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to steadily select the most appropriate strategy to cope with clinical prediction problems. Data mining offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. Therefore, in this study, Liver patient data is considered and evaluated by univariate analysis and a feature selection method for predicator attributes determination. Further comparative study of artificial neural network based predictive models such as BP, RBF, SOM, SVM are provided. Keywords: Medical Informatics, Classification, Liver Data, Artificial Neural Networ

Real time hardware implementation of discrete sliding mode fuzzy controlled buck converter using digital signal processor

This paper deals with the real time hardware implementation of discrete sliding mode fuzzy control (DSMFC) for buck converter using digital signal processor (DSP). Applications like electric vehicle suspension control, flight dynamic control, robot position control and engine throttle position control; sliding mode control (SMC) plays a major role. Hardware realization is difficult with SMC strategy due to the continuous gain change results in chattering problem and actuator or contact may break. To resolve this problem the fuzzy logic (FL) approach has combined with the robust technique discrete sliding mode control (DSMC) to develop a new strategy for DSMFC. The mathematical modeling of the controller is done using MATLAB/Simulink software and practical design of the converter is also realized. The robustness of the controller is proved by introducing sudden change in input voltage as well as load with the help of switching circuit in hardware realization. The obtained practical results are verified by comparing with the simulation output and reference value

Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mode Controller

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn‟t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system

Introduction to Blockchain

The current monetary system has many issues associated with it like double spending, standard transaction fees, financial crisis, centralized power and private ledgers. Blockchain provides a remedy to all these ills by its basic structure, zero or minimal transaction fees and by providing a public ledger system which is visible to everyone who is the part of blockchain which makes it free from complications like double spending and financial cri-sis. Blockchain is basically a continuously growing list of records or public distributed ledger system called blocks linked and secured suing cryptography. Each block has multiple transaction details associated with it. It was introduced in the year 2009 by Satoshi Nakamoto, who is believed to be a Japanese man, born in 1974. Given the features and universal nature of the Blockchain, which include decentralized ledger system, proof of work and cryptography, one can appreciate that its implementation could result in far reaching changes in all domains

Early onset osteoarthritis knee in premature menopausal women

Background: Women with early menopause medical (disease) or surgical (hysterectomy) are having postmenopausal symptoms after a variable period. Osteoarthritis (OA) strikes women more often than men and it increases in prevalence, incidence and severity after menopause. The present study was done to evaluate early onset osteoarthritis knee in premature (early) menopausal women.Methods: We have studied 160 women with early menopause (before 40 yrs of age) developing symptoms and well established osteo arthritic knees. We have studied various factors with early menopause. The data was analysed using SPSS software version 22.Results: In our study 138 cases (86.25%) were surgical menopause (hysterectomy) and 22 cases (13.75%) were medical menopause where definite cause was not obvious. An early onset knee pain was noted in 1 to 2 years. But late OA was noted after 6 to 7 years of menopause. Effective treatment was wanted by majority of the patient from the point of view of post-menopausal osteosaropaenia and physiotherophy. Even in urban population erratic treatment was maximum (75%). Dysfunctional uterine bleeding, fibroid or severe intractable infection appeared be the most common indication for hysterectomy.Conclusions: We concluded that with better awareness of menopause, effective regular treatment and physiotherapy can herald the process of osteoarthritis. The difficulties were mainly developed early because of lack of awareness, no effective regular treatment and physiotherapy. Pain is the starting feature which may continue to severe disability later on

Input-output linearization of DC-DC converter with discrete sliding mode fuzzy control strategy

The major thrust of the paper is on designing a fuzzy logic approach has been combined with a well-known robust technique discrete sliding mode control (DSMC) to develop a new strategy for discrete sliding mode fuzzy control (DSMFC) in direct current (DC-DC) converter. Proposed scheme requires human expertise in the design of the rule base and is inherently stable. It also overcomes the limitation of DSMC, which requires bounds of uncertainty to be known for development of a DSMC control law. The scheme is also applicable to higher order systems unlike model following fuzzy control, where formation of rule base becomes difficult with rise in number of error and error derivative inputs. In this paper the linearization of input-output performance is carried out by the DSMFC algorithm for boost converter. The DSMFC strategy minimizes the chattering problem faced by the DSMC. The simulated performance of a discrete sliding mode fuzzy controller is studied and the results are investigated