Minimization Principle Research Articles

The Optimal Placement of the Head in the Noun Phrase. The Case of Demonstrative, Numeral, Adjective and Noun

ABSTRACT The word order of a sentence is shaped by multiple principles. The principle of syntactic dependency distance minimization is in conflict with the principle of surprisal minimization (or predictability maximization) in single head syntactic dependency structures: while the former predicts that the head should be placed at the centre of the linear arrangement, the latter predicts that the head should be placed at one of the ends (either first or last). A critical question is when surprisal minimization (or predictability maximization) should surpass syntactic dependency distance minimization. In the context of single head structures, it has been predicted that this is more likely to happen when two conditions are met, i.e. (a) fewer words are involved and (b) words are shorter. Here, we test the prediction on the noun phrase when it is composed of demonstrative, numeral, adjective, and noun. We find that, across preferred orders in languages, the noun tends to be placed at one of the ends, confirming the theoretical prediction. We also show evidence of anti-locality effects: syntactic dependency distances in preferred orders are longer than expected by chance.

Investigation of Different Throat Concepts for Precipitation Processes in Saturated Pore-Network Models

AbstractThe development of reliable mathematical models and numerical discretization methods is important for the understanding of salt precipitation in porous media, which is relevant for environmental problems like soil salinization. Models on the pore scale are necessary to represent local heterogeneities in precipitation and to include the influence of solution-air-solid interfaces. A pore-network model for saturated flow, which includes the precipitation reaction of salt, is presented. It is implemented in the open-source simulator DuMu$$^{\textrm{X}}$$ X . In this paper, we restrict ourselves to one-phase flow as a first step. Since the throat transmissibilities determine the flow behaviour in the pore network, different concepts for the decreasing throat transmissibility due to precipitation are investigated. We consider four concepts for the amount of precipitation in the throats. Three concepts use information from the adjacent pore bodies, and one employs a pore-throat model obtained by averaging the resolved pore-scale model in a thin-tube. They lead to different permeability developments, which are caused by the different distribution of the precipitate between the pore bodies and throats. We additionally apply two different concepts for the calculation of the transmissibility. One obtains the precipitate distribution from analytical assumptions, the other from a geometric minimization principle using a phase-field evolution equation. The two concepts do not show substantial differences for the permeability development as long as simple pore-throat geometries are used. Finally, advantages and disadvantages of the concepts are discussed in the context of the considered physical problem and a reasonable effort for the implementation and computational costs.

Interpretable Machine Learning-Based Influence Factor Identification for 3D Printing Process-Structure Linkages.

Three-dimensional printing technology is a rapid prototyping technology that has been widely used in manufacturing. However, the printing parameters in the 3D printing process have an important impact on the printing effect, so these parameters need to be optimized to obtain the best printing effect. In order to further understand the impact of 3D printing parameters on the printing effect, make theoretical explanations from the dimensions of mathematical models, and clarify the rationality of certain important parameters in previous experience, the purpose of this study is to predict the impact of 3D printing parameters on the printing effect by using machine learning methods. Specifically, we used four machine learning algorithms: SVR (support vector regression): A regression method that uses the principle of structural risk minimization to find a hyperplane in a high-dimensional space that best fits the data, with the goal of minimizing the generalization error bound. Random forest: An ensemble learning method that constructs a multitude of decision trees and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. GBDT (gradient boosting decision tree): An iterative ensemble technique that combines multiple weak prediction models (decision trees) into a strong one by sequentially minimizing the loss function. Each subsequent tree is built to correct the errors of the previous tree. XGB (extreme gradient boosting): An optimized and efficient implementation of gradient boosting that incorporates various techniques to improve the performance of gradient boosting frameworks, such as regularization and sparsity-aware splitting algorithms. The influence of the print parameters on the results under the feature importance and SHAP (Shapley additive explanation) values is compared to determine which parameters have the greatest impact on the print effect. We also used feature importance and SHAP values to compare the importance impact of print parameters on results. In the experiment, we used a dataset with multiple parameters and divided it into a training set and a test set. Through Bayesian optimization and grid search, we determined the best hyperparameters for each algorithm and used the best model to make predictions for the test set. We compare the predictive performance of each model and confirm that the extrusion expansion ratio, elastic modulus, and elongation at break have the greatest influence on the printing effect, which is consistent with the experience. In future, we will continue to delve into methods for optimizing 3D printing parameters and explore how interpretive machine learning can be applied to the 3D printing process to achieve more efficient and reliable printing results.

Improved PID based Adaptive Controllers for Denoising Biomedical Signals

Biomedical signal processing is one of the most popular research domains. Very fine features in biomedical signals carry important information regarding patient’s health. So, it is necessary to have noise free biomedical signals for the correct diagnosis. The major trouble for biomedical equipment is Power Line Interference (PLI) which impairs the signals. An adaptive filter can be one of the possible solutions for the removal of non-stationary noise, but maintaining the system stability along with a high convergence rate is a critical issue. The adaptive algorithm works on the principle of minimization of error for optimized coefficients updating while PID controller attempts to minimize the error over time by adjusting the control variables. In this paper, these two different approaches are combined to get an efficient solution for adaptive PLI cancellation and two new algorithms namely PID-based Response Adjustment for Reducing Error (PID-RARE) and PID-based Coefficient Adjustment for Reducing Error (PID-CARE) are proposed. The integration of NSLMS adaptive algorithm with PID controller in the proposed algorithms are found to be an effective solution to adaptive PLI cancellation and have shown quite better performance in terms of SNRout, correlation coefficient, mean square error thereby providing more cleaner signal at lesser convergence rate.

Mixed variational formulation and finite-element implementation of second-order poro-elasticity

We present a variational formulation of second-order poro-elasticity that can be readily implemented into finite-element codes by using standard Lagrangian interpolation functions. Point of departure is a two-field minimization principle in terms of the displacement and the fluid flux as independent variables. That principle is taken as a basis for the derivation of continuous and incremental saddle-point formulations in terms of an extended set of independent variables. By static condensation this formulation is then reduced to a minimization principle in terms of the displacement and fluid flux as well as associated higher-order fields. Once implemented into a finite-element code, the resulting formulation can be applied to the numerical simulation of porous media in consideration of second-order effects. Here, we analyze the model response by means of several example problems including two standard tests in poro-elasticity, namely the consolidation problems of Terzaghi and Mandel, and compare the results with those of a corresponding first-order model. As becomes clear, the second-order formulation can unleash its full potential when applied to the study of porous media having spatial dimensions comparable to the size of their microstructure. In particular, it is capable to regularize steep field gradients at external as well as internal surfaces and to describe material dilatation effects known from experiments.

Online imbalance learning with unpredictable feature evolution and label scarcity

Recently, online learning with imbalanced data streams has aroused wide concern, which reflects an uneven distribution of different classes in data streams. Existing approaches have conventionally been conducted on stationary feature space and they assume that we can obtain the entire labels of data streams in the case of supervised learning. However, in many real scenarios, e.g., the environment monitoring task, new features flood in, and old features are partially lost during the changing environment as the different lifespans of different sensors. Besides, each instance needs to be labeled by experts, resulting in expensive costs and scarcity of labels. To address the above problems, this paper proposes a novel Online Imbalance learning with unpredictable Feature evolution and Label scarcity (OIFL) algorithm. First, we utilize margin-based online active learning to selectively label valuable instances. After obtaining the labels, we handle imbalanced class distribution by optimizing F-measure and transforming F-measure optimization into a weighted surrogate loss minimization. When data streams arrive with augmented features, we combine the online passive-aggressive algorithm and structural risk minimization to update the classifier in the divided feature space. When data streams arrive with incomplete features, we leverage variance to identify the most informative features following the empirical risk minimization principle and continue to update the existing classifier as before. Finally, we obtain a sparse but reliable learner by the strategy of projecting truncation. We derive theoretical analyses of OIFL. Also, experiments on the synthetic datasets and real-world data streams to validate the effectiveness of our method.

Prawne i praktyczne aspekty dotyczące przetwarzania danych osobowych w procesie rekrutacji

The subject of this article is to identify good practices, occurring risks and irregularities in the processing of personal data of job candidates as part of the employee recruitment process and to develop principles for verifying the correctness of the functioning of this process as a tool supporting the audit task. The recruitment process and the associated staff turnover is one of the key areas where the employer should ensure the organizational and legal security of the processed personal data, which undeniably affects its professionalism and the perception of external customers, such as job candidates. The uniformity of the legality of this process is influenced by both national labor laws and EU regulations. The key principles to ensure the compliance of processed data with the requirements of labor laws in the recruitment process are the principle of data minimization and storage limitation, as well as the processing of personal data of job candidates by persons only authorized by the employer, who have committed themselves to the principles of confidentiality and impartiality. The above is also related to the implementation of the controller’s information obligations to job candidates and their correlation with the actual processing of personal data by the controller and its authorized personnel.

Generalized Federated Learning via Gradient Norm-Aware Minimization and Control Variables

Federated Learning (FL) is a promising distributed machine learning framework that emphasizes privacy protection. However, inconsistencies between local optimization objectives and the global objective, commonly referred to as client drift, primarily arise due to non-independently and identically distributed (Non-IID) data, multiple local training steps, and partial client participation in training. The majority of current research tackling this challenge is mainly based on the empirical risk minimization (ERM) principle, while giving little consideration to the connection between the global loss landscape and generalization capability. This study proposes FedGAM, an innovative FL algorithm that incorporates Gradient Norm-Aware Minimization (GAM) to efficiently search for a local flat landscape. FedGAM specifically modifies the client model training objective to simultaneously minimize the loss value and first-order flatness, thereby seeking flat minima. To directly smooth the global flatness, we propose the more significant FedGAM-CV, which employs control variables to correct local updates, guiding each client to train models in a globally flat direction. Experiments on three datasets (CIFAR-10, MNIST, and FashionMNIST) demonstrate that our proposed algorithms outperform existing FL baselines, effectively finding flat minima and addressing the client drift problem.

Design of active vibration isolation controller for pointing stabilization mechanism based on feedforward–feedback hybrid control

The use of space technology and small space loads is increasingly common. To address this, a mechanism for isolation vibration in small optical load has been proposed. The mechanism includes a coarse and fine stage parallel pointing platform (CFPP). This paper investigates an active vibration isolation scheme for the novel pointing stabilization mechanism. The kinetic energy minimization principle is derived from the analysis of its working mechanism and dynamic feedforward characteristics. This principle is confirmed by the feedforward of the indeterminate degrees of freedom of the under-constrained mechanism. A hybrid control scheme of feedback and feedforward is developed based on the H∞ algorithm and the optimal feedforward control algorithm. Simulation and experimentation have proven that the vibration isolation efficiency of more than 20 dB can be achieved in all three axis rotation directions. This meets the precision pointing requirements of small optical load effectively.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.

A mathematical theory of relational generalization in transitive inference

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Brain asymmetry as minimization of free energy: a theoretical model.

The asymmetry between the left and right sides seems to be a general principle of organization of the nervous systems in Bilateria, providing the foundations for a plethora of leftward and rightward biases in behaviour as documented in species ranging from Caenorhabditis elegans nematodes to humans. Several theories have been put forward to account for the existence and maintenance in the evolution of the asymmetric organization of the brain at both individual and population levels. However, what is missing in theorizing about the evolution of brain asymmetry is an overarching general hypothesis that may subsume all different aspects of current models. Here, we tried to provide an overarching general framework based on the energy and free-energy minimization principle, which proved so valuable in other areas of neuroscience. We found that at the individual level the antisymmetric singlet configuration realizes the lowest energy state of the system, whereas at the group level, the spontaneous emergence of directional asymmetry arises as a consequence of the minimization of the free energy of the system, which guarantees its stability and equilibrium. We thus argue that the various phenomenological aspects of brain asymmetry that have been captured in biology-e.g. sparing of neural tissue, control of unitary motor responses and, at the population level, evolutionarily stable strategies described by mathematical games theory-may be thought of as the manifestation of a more general principle of energy minimization generating, among others, asymmetry of the brains.

Pre-disaster evacuation network design with uncertain demand and behavioural choices: A bi-criteria generalized cost perspective

Pre-disaster evacuation network design is critical to improving evacuation efficiency and reducing the loss of life and property caused by disasters. However, evacuation network design is complicated by the uncertainty of pre-disaster evacuation demand and the heterogeneity of evacuees’ route choices when faced with a disaster scenario. Therefore, how to design efficient evacuation networks considering these uncertainties has become an important research topic. In this paper, we study the pre-disaster flood evacuation network design problem, define a bi-criteria generalized cost metric based on Evacuation Travel Time (ETT) and Evacuation Network Congestion Degree (ENCD), and develop a model that considers uncertain demand and evacuation behavioural choices to minimize the generalized cost in the evacuation network. For the uncertain behavioural choice, we propose an Expanded Stochastic User Equilibrium (ESUE) model based on the Random Regret Minimization (RRM) principle and Boundedly Rational User Equilibrium (BRUE) conditions to describe it. For the uncertain demand, we design a Distributionally Robust Optimization (DRO) method based on Mean Absolute Deviation (MAD) to deal with this uncertain model. This paper designs an Improved Method of Successive Averages Combined with Genetic Algorithm (IMSA-GA) to solve the model and uses the Nguyen-Dupuis test network to verify the applicability and feasibility of the model and algorithm. In addition, we also conduct an evacuation network design in the Hawkesbury-Nepean region of Australia. The larger the threshold value based on the regret utility ratio, the smaller the generalized cost value, which indicates the behavioural choices of the evacuees have an impact on the evacuation network design, and the generalized cost decreases by 6.12% when the threshold value is increased from 0.1 to 0.9. In addition, the generalized cost value obtained by IMSA-GA will be 0.0025 lower than Improved Method of Successive Averages Combined with Simulated Annealing (IMSA-SA) and 0.0005 lower than Improved Method of Successive Averages Combined with Taboo Search (IMSA-TS). The theoretical contribution of this paper is to find an approach to the uncertain evacuation network design problem, especially to improve the practicality of the model by fully considering evacuation behaviour. The practical contribution is that our findings can provide insights for government authorities to design efficient and reliable evacuation networks that balance evacuation travel time and evacuation network congestion. In the future, if evacuation behavioural choices can be analysed based on actual surveys, the estimation of behavioural parameters based on survey data will be more instructive for the evacuation process.

DECODING THE SEMANTIC VOLUME OF NOUNS WITH THE VERBAL MODALITY MÜSSEN: THE SYNERGISTIC DIMENSION OF LEAST EFFORT

The presented work is one of the stages of noun research within a certain verbal modality. The focus of the article lies in the study of the generating noun content in relation to the verbal modality müssen in German from the perspective of linguosynergetic scientific paradigm, which considers language as a complex dynamic system capable of self-organization and self-regulation. In this study methodologically important is the position that the synergetic law of making the least effort and conservation of speech energy is the driving factor of language development. In a synergetic sense, the author of the article draws parallels between the dictionary as a representative of the linguistic generalization of the structured amount of knowledge of the learned extraverbal reality and the mental lexicon, which is not an arbitrary accumulation of contributions, but constitutes a structured hierarchical system of such contributions. If lexical units in the synergetic sense and their meanings at the language level contain coded structured information, then in the same aspect the paper considers the implementation of the word in one of its meanings at the speech level as information decoding. The linguosynergetic format of the research proposed in this article assumes that language as a self-regulating system under the influence of external energy and information has developed a mechanism of different representations of nouns in syntactic constructions with different verbal modality in German-language fiction and press. The results obtained on the basis of the synergetic-quantitative approach are extrapolated to linguosynergetic models of self-organization of noun names according to the principle of minimization of efforts, which directs the human mental lexicon to optimal decoding of semantic volume of polysemic or monosemic nouns in relation to the verbal modality müssen in German-language fiction and press. It was proved that since the closest distances in the lexical structure of a word at the linguistic level are the closest in comparison with the structures of knowledge of the human mental lexicon, the construction of noun content in modern German on the synergetic principle of conservation of speech energy and minimization of efforts is the most optimal way of directing the mental lexicon of a person to decoding the semantic scope of the closets (main) meanings of the polysemic and unambiguous monosemic word models.

Thermodynamic Model for Hydrogen Production from Rice Straw Supercritical Water Gasification.

Supercritical water gasification (SCWG) technology is highly promising for its ability to cleanly and efficiently convert biomass to hydrogen. This paper developed a model for the gasification of rice straw in supercritical water (SCW) to predict the direction and limit of the reaction based on the Gibbs free energy minimization principle. The equilibrium distribution of rice straw gasification products was analyzed under a wide range of parameters including temperatures of 400-1200 °C, pressures of 20-50 MPa, and rice straw concentrations of 5-40 wt%. Coke may not be produced due to the excellent properties of supercritical water under thermodynamic constraints. Higher temperatures, lower pressures, and biomass concentrations facilitated the movement of the chemical equilibrium towards hydrogen production. The hydrogen yield was 47.17 mol/kg at a temperature of 650 °C, a pressure of 25 MPa, and a rice straw concentration of 5 wt%. Meanwhile, there is an absorptive process in the rice straw SCWG process for high-calorific value hydrogen production. Energy self-sufficiency of the SCWG process can be maintained by adding small amounts of oxygen (ER < 0.2). This work would be of great value in guiding rice straw SCWG experiments.

Bats monitoring: a classification procedure of bats behaviours based on Hawkes processes

Abstract We are interested in the problem of classifying commuting and foraging behaviour of bats at delimited geographical areas, namely sites, throughout France. To predict the majority behaviour on these sites, we use echolocation call data recorded as part of Vigie-Chiro participatory project. As the temporal distribution of calls is a relevant indicator of behaviour, providing an adequate model of this distribution is a matter of great interest. Given the self-exciting dynamics observed in foraging behaviour, we propose to model bat calls by Hawkes processes. Specifically, we consider that the start time of each call emitted on a site is an event of a Hawkes process. Taking advantage of this modelling, we use a suitable procedure that relies on the empirical risk minimization principle to discriminate between the 2 classes. Then, the performance of the procedure is assessed on synthetic data through comprehensive numerical experiments. The overall methodology is evaluated with a goodness-of-fit test. Finally, we present the obtained results on the real data set. The classification results are convincing and show the relevance of our method, which could contribute to a better understanding of behavioural determinants and open up broad perspectives in spatial ecology.

Conglomerate rock interface debonding characteristics and damage evolution based on cohesive phase-field method

Rock failure often occurs due to uneven mechanical strength and discontinuous strain at the interface. However, due to the complex interaction mechanism between different phases of conglomerate, it is necessary to improve the crack growth model at the conglomerates' interface. In view of the physical structure and mechanical properties of conglomerate materials, this paper innovatively adopts the cohesive phase field model to study the material deformation and crack growth of conglomerate materials. Different from the traditional phase field model, this model characterises the real structure of the interface by adding an interface phase field, which is used to simulate the debonding of the interface during crack propagation and damage evolution. The regularization of the phase field is carried out to ensure the smoothness of the interface. The transition state of the interface region is determined by the element and the additional phase field. The energy equation is coupled with the crack propagation process using the crack surface density function. The evolution of the damaged phase field and displacement field is driven by the principle of energy minimization. The innovative assumption is that there is a virtual crack at the crack tip, and the interface related elements show a certain cohesive softening form during the damage evolution, which is the main way to realize the particularity of interface mechanics. By controlling the crack dispersion state and stiffness degradation of the material, several ideal softening models are innovatively assumed according to the cohesive force change law to satisfy the mechanical properties and stress function constraints. When cutting blocks n=2.5×104, the errors of the linear model, exponential model, and compound inverse model are 1.76%, 7.48%, and 5.19%, respectively. The results of the exponential model agree well with the stress strength (the error is 2%) and interface crack kinking angle (68.89°). The simulation results indicate that the deformation process of the conglomerate can be divided into three stages: elastic deformation stage, crack propagation stage, and stable load stage. The tensile strength of conglomerate is positively correlated with interface's fracture toughness and proportion of gravel area, respectively.

Peak-to-average power ratio minimization and complexity reduction in MIMO-OFDM systems using spatial circular shifting and temporal interleaving method

Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) technological support for the simultaneous and frequent access by a large number of users to radio resources. For 5G cellular systems, this exhaust is not enough to provide physical layer services. An appropriate Peak-to-average power ratio (PAPR) minimization principle, which maximizes data capacity and channel utility, has been used to address this issue. In this paper, mainly focus on minimize the high PAPR of the candidate sequence of the OFDM sub-block using modified enhancement asymmetric arithmetic coding scheme (M-EAAC). According to this, circular shifting of the candidate sequence is established in the spatial circular shifting and temporal interleaving (SCS-TI) form to generated different set of conjugated phases which is multiplied with candidate sequence. Then, the transmitting antenna is identified the best lowest PAPR of the candidate sequence is chosen for entire OFDM data transmission. The simulation results conveys that the proposed SCS-TI method provide acceptable improvement in the PAPR reduction as compared with conventional selective mapping(SLM)and pseudo-random SLM(PR-SLM). Moreover, the complexity evaluation which ensure the proposed method provides better improvement at three important stages includes inverse fast Fourier transform (IFFT) operation, optimization process, and PAPR calculation at each candidate sequence.

IMPLEMENTASI ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI KOMENTAR SPAM PADA INSTAGRAM

Instagram is a photo and video sharing application that allows users to take photos, take videos, apply digital filters, and share them on various social networking services. Public figures, especially actors, actresses, influencers and Instagram celebrities, use Instagram as a platform to promote their various activities or works to their fans or followers. However, there are a lot of spam comments on these posts, both good and bad. This research obtained comment data from Instagram to be used as a dataset, namely from public figures, actresses and actors who have more than 5 million followers. By using preprocessing (data cleansing, tokenization, case folding and stopword removal) and TF-IDF for weighting, selecting the kernel you want to use and training the SVM model in the classification process. We need a system that can classify whether comment are spam or not spam. The method used is Support Vector Machine is a machine learning method that has the working principle of structural risk minimization aimed at finding hyperplanes or separators between two classes in an input space. From the test results it was found that the performance of the proposed an accuracy level of 96.82%, precision 94%, recall 92%, and f1-score 93%.