Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Development of a Post Quantum Encryption Key Generation Algorithm Using Electromagnetic Wave Propagation Theory

In today’s rapid widespread of digital technologies into all live aspects to enhance efficiency and productivity on the one hand and on the other hand ensure customer engagement, personal data counterfeiting has become a major concern for businesses and end-users. One solution to ensure data security is encryption, where keys are central. There is therefore a need to find robusts key generation implementation that is effective, inexpensive and non-invasive for protecting and preventing data counterfeiting. In this paper, we use the theory of electromagnetic wave propagation to generate encryption keys.

Deep Transfers of p-Class Tower Groups

Let p be a prime. For any finite p-group G, the deep transfers T H,G ' : H / H ' → G ' / G " from the maximal subgroups H of index (G:H) = p in G to the derived subgroup G ' are introduced as an innovative tool for identifying G uniquely by means of the family of kernels ùd(G) =(ker(T H,G ')) (G: H) = p. For all finite 3-groups G of coclass cc(G) = 1, the family ùd(G) is determined explicitly. The results are applied to the Galois groups G =Gal(F3 (∞)/ F) of the Hilbert 3-class towers of all real quadratic fields F = Q(√d) with fundamental discriminants d > 1, 3-class group Cl3(F) □ C3 × C3, and total 3-principalization in each of their four unramified cyclic cubic extensions E/F. A systematic statistical evaluation is given for the complete range 1 d 7, and a few exceptional cases are pointed out for 1 d 8.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation

Two new regularization algorithms for solving the first-kind Volterra integral equation, which describes the pressure-rate deconvolution problem in well test data interpretation, are developed in this paper. The main features of the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input data. In both algorithms, the solution is represented as decomposition on special basic functions, which satisfy given a priori information on solution, and this idea allow us significantly to improve the quality of approximate solution and simplify solving the minimization problem. The theoretical details of the algorithms, as well as the results of numerical experiments for proving robustness of the algorithms, are presented.

Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.

基于遗传算法和最小二乘支持向量机的织物剪切性能预测

提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力. Abstract： A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Enhanced Euclid Algorithm for Modular Multiplicative Inverse and Its Application in Cryptographic Protocols

Numerous cryptographic algorithms (ElGamal, Rabin, RSA, NTRU etc) require multiple computations of modulo multiplicative inverses. This paper describes and validates a new algorithm, called the Enhanced Euclid Algorithm, for modular multiplicative inverse (MMI). Analysis of the proposed algorithm shows that it is more efficient than the Extended Euclid algorithm (XEA). In addition, if a MMI does not exist, then it is not necessary to use the Backtracking procedure in the proposed algorithm;this case requires fewer operations on every step (divisions, multiplications, additions, assignments and push operations on stack), than the XEA. Overall, XEA uses more multiplications, additions, assignments and twice as many variables than the proposed algorithm.

Parallel Minimax Searching Algorithm for Extremum of Unimodal Unbounded Function

In this paper we consider a parallel algorithm that detects the maximizer of unimodal function f(x) computable at every point on unbounded interval (0, ∞). The algorithm consists of two modes: scanning and detecting. Search diagrams are introduced as a way to describe parallel searching algorithms on unbounded intervals. Dynamic programming equations, combined with a series of liner programming problems, describe relations between results for every pair of successive evaluations of function f in parallel. Properties of optimal search strategies are derived from these equations. The worst-case complexity analysis shows that, if the maximizer is located on a priori unknown interval (n-1], then it can be detected after cp(n)=「2log「p/2」+1(n+1)」-1 parallel evaluations of f(x), where p is the number of processors.

Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm

This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

Effect of a region-wide incorporation of an algorithm based on the 2012 international consensus guideline on the practice pattern for the management of pancreatic cystic neoplasms in an integrated health system

AIM To examine the practice pattern in Kaiser Permanente Southern California(KPSC), i.e., gastroenterology(GI)/surgery referrals and endoscopic ultrasound(EUS), for pancreatic cystic neoplasms(PCNs) after the regionwide dissemination of the PCN management algorithm.METHODS Retrospective review was performed; patients with PCN diagnosis given between April 2012 and April 2015(18 mo before and after the publication of the algorithm) in KPSC(integrated health system with 15 hospitals and 202 medical offices in Southern California) were identified.RESULTS2558(1157 pre-and 1401 post-algorithm) received a new diagnosis of PCN in the study period. There was no difference in the mean cyst size(pre-19.1 mm vs post-18.5 mm, P = 0.119). A smaller percentage of PCNs resulted in EUS after the implementation of the algorithm(pre-45.5% vs post-34.8%, P < 0.001). A smaller proportion of patients were referred for GI(pre-65.2% vs post-53.3%, P < 0.001) and surgery consultations(pre-24.8% vs post-16%, P < 0.001) for PCN after the implementation. There was no significant change in operations for PCNs. Cost of diagnostic care was reduced after the implementation by 24%, 18%, and 36% for EUS, GI, and surgery consultations, respectively, with total cost saving of 24%.CONCLUSION In the current healthcare climate, there is increased need to optimize resource utilization. Dissemination of an algorithm for PCN management in an integrated health system resulted in fewer EUS and GI/surgery referrals, likely by aiding the physicians ordering imaging studies in the decision making for the management of PCNs. This translated to cost saving of 24%, 18%, and 36% for EUS, GI, and surgical consultations, respectively, with total diagnostic cost saving of 24%.

Comparative analysis of various modularization algorithms and species specific study of VEGF signaling pathways

In biology, signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. It involves ordered sequences of biochemical reactions inside the cell. These cascades of reactions are carried out by enzymes and activated by second messengers. Signal transduction pathways are complex in nature. Each pathway is responsible for tuning one or more biological functions in the intracellular environment as well as more than one pathway interact among themselves to carry forward a single biological function. Such kind of behavior of these pathways makes understanding difficult. Hence, for the sake of simplicity, they need to be partitioned into smaller modules and then analyzed. We took VEGF signaling pathway, which is responsible for angiogenesis for this kind of modularized study. Modules were obtained by applying the algorithm of Nayak and De (Nayak and De, 2007) for different complexity values. These sets of modules were compared among themselves to get the best set of modules for an optimal complexity value. The best set of modules compared with four different partitioning algorithms namely, Farhat’s (Farhat, 1998), Greedy (Chartrand and Oellermann, 1993), Kernighan-Lin’s (Kernighan and Lin, 1970) and Newman’s community finding algorithm (Newman, 2006). These comparisons enabled us to decide which of the aforementioned algorithms was the best one to create partitions from human VEGF signaling pathway. The optimal complexity value, on which the best set of modules was obtained, was used to get modules from different species for comparative study. Comparison among these modules would shed light on the trend of development of VEGF signaling pathway over these species.

Multinucleated giant cells of bladder mucosa are modified telocytes:Diagnostic and immunohistochemistry algorithm and relation to PDL1 expression score

BACKGROUND Multinucleated giant cells(MGCs)in bladder carcinomas are poorly studied.AIM To describe the function,morphogenesis,and origin of mononuclear and MGCs in urothelial carcinoma(UC)of the bladder in Bulgarian and French patients.METHODS Urothelial bladder carcinomas(n=104)from 2016-2020 were analyzed retrospectively using immunohistochemical(IHC)and histochemical stain examination.Giant cells in the bladder stroma were found in 35.6%of cases,more often in highgrades.RESULTS We confirm that MGCs in the mucosa in UC of the bladder were positive for both mesenchymal and myofibroblast markers(vimentin,smooth muscle actin,Desmin,and CD34)and the macrophage marker CD68.Furthermore,IHC studies revealed the following profile of these cells:Positive for p16;negative for epithelial(CK AE1/AE3 and GATA-3),vascular(CD31),neural(PS100 and CKIT),cambial,blastic(CD34-blasts and C-KIT),and immune markers(IG G,immunoglobulin G4,and PD-L1);no proliferative activity,possess no specific immune function,and cannot be used to calculate the Combined Positive Score scale.CONCLUSION In conclusion,the giant stromal cells in non-tumor and tumor bladder can be used as a characteristic and relatively constant,although nonspecific,histological marker for chronic bladder damage,reflecting the chronic irritation or inflammation.Likewise,according to the morphological and IHC of the mono-and multinucleated giant cells in the bladder,they are most likely represent telocytes capable of adapting their morphology to the pathology of the organ.

On Over-Relaxed Proximal Point Algorithms for Generalized Nonlinear Operator Equation with (A,η,m)-Monotonicity Framework

In this paper, a new class of over-relaxed proximal point algorithms for solving nonlinear operator equations with (A,η,m)-monotonicity framework in Hilbert spaces is introduced and studied. Further, by using the generalized resolvent operator technique associated with the (A,η,m)-monotone operators, the approximation solvability of the operator equation problems and the convergence of iterative sequences generated by the algorithm are discussed. Our results improve and generalize the corresponding results in the literature.

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

Primary hyperparathyroidism-induced acute pancreatitis in pregnancy:A systematic review with a diagnostic-treatment algorithm

BACKGROUND Primary hyperparathyroidism(PHPT)-induced acute pancreatitis(AP)during pregnancy has rarely been described.Due to this rarity,there are no diagnostic or treatment algorithms for pregnant patients.AIM To determine appropriate diagnostic methods,therapeutic options,and factors related to maternal and fetal outcomes for PHPT-induced AP in pregnancy.METHODS A literature search of articles in English,Japanese,German,Spanish,and Italian was performed using PubMed(1946-2023),PubMed Central(1900-2023),and Google Scholar.The Preferred Reporting Items for Systematic reviews and Meta-Analyses(PRISMA)protocol was followed.The search terms included“pancreatite acuta,”“iperparatiroidismo primario,”“gravidanza,”“travaglio,”“puerperio,”“postpartum,”“akute pankreatitis,”“primärer hyperparathyreoidismus,”“Schwangerschaft,”“Wehen,”“Wochenbett,”“pancreatitis aguda,”“hiperparatiroidismo primario,”“embarazo,”“parto,”“puerperio,”“posparto,”“acute pancreatitis,”“primary hyperparathyroidism,”“pregnancy,”“labor,”“puerperium,”and“postpartum.”Additional studies were identified by reviewing the reference lists of retrieved studies.Demographic,imaging,surgical,obstetric,and outcome data were obtained.RESULTS Fifty-four cases were collected from the 51 studies.The median maternal age was 29 years.PHPT-induced AP starts at the 20th gestational week;higher gestational weeks were seen in mothers who died(mean gestational week 28).Median values of amylase(1399,Q1-Q3=519-2072),lipase(2072,Q1-Q3=893-2804),serum calcium(3.5,Q1-Q3=3.1-3.9),and parathormone(PTH)(384,Q1-Q3=123-910)were reported.In 46 cases,adenoma was the cause of PHPT,followed by 2 cases of carcinoma and 1 case of hyperplasia.In the remaining 5 cases,the diagnosis was not reported.Neck ultrasound was positive in 34 cases,whereas sestamibi was performed in 3 cases,and neck computed tomography or magnetic resonance imaging was performed in 9 cases(the enlarged parathyroid gland was not localized in 3 cases).Surgery was the preferred treatment during pregnancy in 33 cases(median week of gestation 25,Q1-Q3=20-30)and postpartum in 12 cases.The timing was not reported in the remaining 9 cases,or surgery was not performed.AP was managed surgically in 11 cases and conservatively in 43(79.6%)cases.Maternal and fetal mortality was 9.3%(5 cases).Surgery was more common in deceased mothers(60.0%vs 16.3%;P=0.052),and PTH values tended to be higher in this group(910 pg/mL vs 302 pg/mL;P=0.059).Maternal mortality was higher with higher serum lipase levels and earlier delivery week.Higher calcium(4.1 mmol/L vs 3.3 mmol/L;P=0.009)and PTH(1914 pg/mL vs 302 pg/mL;P=0.003)values increased fetal/child mortality,as well as abortions(40.0%vs 0.0%;P=0.007)and complex deliveries(60.0%vs 8.2%;P=0.01).CONCLUSION If serum calcium is not tested during admission,definitive diagnosis of PHPT-induced AP in pregnancy is delayed,while early diagnosis and immediate intervention lead to excellent maternal and fetal outcomes.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.