A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure.Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings.In the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure states.While effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between them.Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert system.This granularity is essential for improving patient-specific interventions and developing proactive seizure management strategies.This study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure stages.To enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and robustness.Moreover,k-fold cross-validation ensures the model’s reliability and generalizability across different data sets.Trained and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG signals.In summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinical settings.With its inherent classification performance,the proposed approach represents a significant step forward in improving patient outcomes through advanced AI techniques.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

High Speed Network Intrusion Detection System(NIDS)Using Low Power Precomputation Based Content Addressable Memory

NIDS(Network Intrusion Detection Systems)plays a vital role in security threats to computers and networks.With the onset of gigabit networks,hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS.The software-based system limits parallel execution,which in turn confines the performance of a modern network.This paper presents a signature-based lookup technique using reconfigurable hardware.Content Addressable Memory(CAM)is used as a lookup table architecture to improve the speed instead of search algorithms.To minimize the power and to increase the speed,pre-computation based CAM(PBCAM)can be used,as this technique avoids repeated search comparisons.PBCAM employs the two-stage comparison with a parameter memory in the first stage and data memory in the second stage.Only the matched data in the parameter memory are compared in the data memory.This reduces the number of comparisons,thereby increasing the speed of the system.In this work dual-port RAM-based PBCAM(DP-PBCAM)is used to design a signature-based intrusion detection system.A low power parameter extractor is used with a minimum number of gates for precomputation.The hardware implementation is done using Xilinx Spartan 3E FPGA.The proposed DP-PBCAM lookups support a gigabit-speed of 7.42 Gbps.

Analysis of Efficient 32 Bit Adder Using Tree Grafting Technique

Adder with high efficiency and accuracy is the major requirement for electronic circuit design.Here the optical logic gate based adder circuit is designed for better performance analysis of optical input signals varied with the wavelength.Efficiency of the adder can be improved by increasing the speed of operation,reducing the complexity and power consumption.To maintain the high efficiency with accuracy,a new combination of adder has been proposed and tested in this work.A new adder by combining the logics of Brent Kung,Sklansky and Kogge Stone adders by Tree Grafting Technique(BSKTGT)has been tested along with individual Brent Kung,Sklansky,Kogge Stone,Knowles,Han Carlson and Ladner Fischer adders.All the existing and proposed adders have been designed and tested for efficiency with the help of Cadence platform with 45 nm technology.Efficiency in terms of Size reduction,Power reduction,Power Delay Product(PDP)and accuracy in adding 8 bit,16 bit and 32 bit values had been tested for all the adders and found that the 32 bit BSKTGT adder performed well in all aspects and have produced better efficiency with the power consumption of 52.512426μW with 3.16%of power saving over Brent Kung adder,utilised an area of 631.191 with 8.55%reduction over Kogge Stone Adder,has the cell count of 132 which is 10.61%reduction over Brent Kung Adder and PDP value of 122.6695 J,which is 0.46%less than that of the Han Carlson Adder.

Battle Royale Optimization-Based Resource Scheduling Scheme for Cloud Computing Environment

Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.