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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Detection of oral cancer lesions using hybrid classifier

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AIP Conference Proceedings 2023年第1期2591卷

作者： K. Karpagavadivu C. R. Rathish P. Sindhuja A. Kousalya 1Department of Computer Science Engineering Institute of Technology Coimbatore India 2Department of Computer Engineering New Horizon College of Engineering Bengaluru India 3Department of Computer Science Engineering United Institute of Technology Coimbatore India 4Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India

Oral cancer is a main worldwide health problem accounting for 606,520 deaths in 2020, and it is most predominant in the middle-and low-income nations. Permitting computerization in identifying potentially malignant and malignant lesions in the oral cavity would possibly result in low-expense and early detection of the disease. The most important purpose of this research is to find the Oral Cancer Lesions affected region in the tongue images. The current work utilized the GVF algorithm to detect Oral Cancer Lesions using features involved in tongue images. This article offers a novel approach to merging bounding box annotations from different medical practitioners. Additionally, gradient vector flow was used to segment images, where the complicated patterns have been obtained for tackling this difficult task. Using the initial data gathered in this study, a hybrid classifier algorithm was assessed to detect Oral cancer lesions, and features like colour, texture and geometry were extracted. BioMed Chinese Medicine Repository collects the tongue images. Additionally, performances are described categorizing as per the kind of referral decision. Our initial findings establish support vector machine has the probability of challenging this stimulating task.

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The development of autonomous drone for document delivery in IPB University

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AIP Conference Proceedings 2023年第1期2482卷

作者： Carli Apriansyah Hutagalung Karlisa Priandana Kudang Boro Seminar 1Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Indonesia 2Department of Mechanical and Biosystem Engineering Faculty of Agricultural Engineering and Technology IPB University Indonesia

This study aims to design an autonomous hexacopter drone that can be used to deliver documents in IPB University, to replace conventional oil-fueled transportation. The hexacopter drone is designed with autopilot control using a point-to-point flight system based on GPS coordinates. Preliminary research was conducted to examine the delivery of documents between the Faculty of Mathematics and Natural Sciences (FMIPA) and the Faculty of Agricultural Technology (FATETA). The experimental results showed that the maximum payload of the designed drone is 1 kg. The drone flight tests showed that the drone can reach its destination with good performance. The average error for the unloaded flight test from FMIPA to FATETA is 1.02 m, while from FATETA to FMIPA is 0.53 m. Meanwhile, the average error for flight tests with a maximum payload (1 kg) from FMIPA to FATETA is 1.97 m, while from FATETA to FMIPA is 0.703 m.

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Malnutrition detection in infants using machine learning approach

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AIP Conference Proceedings 2022年第1期2424卷

作者： Rakhi Wajgi Dipak Wajgi 1Department of Computer Technology YCCE Nagpur Maharashtra India 2Department of Computer Engineering STVPCET Nagpur Maharashtra India

Balanced nutrition is the main source of energy. It is necessary for healthy life of people. Healthy nutrients enable cells to perform their regular activities at pace. Deficiency of proper nutrition while birth causes various complications in further life. These complications include wasting, stunting, edema, mental illness, low immune system, ridged or spoon-shaped nails, brittle, dry hair, and underweight etc. Malnutrition is a condition that occurs when a person consumes a diet that is deficient in one or more major nutrients, or has too many of them. Marasmus, kwashiorkor and intermediate states of marasmus-kwashiorkor are included in the term Protein-Energy Malnutrition (PEM) disorders. PEM is the cause of underweight (low weight for age), stunting (low height for age), and wasting (low weight for height). In India, stunting affects 48% of infants under five years age, wasting affects 20%, and underweight affects 43%. Most children suffering from undernutrition in mild to moderate forms are unnoticed in India, which affects their growth at early ages. Detecting malnutrition at early stage reduces further healthcare cost and improve health outcome. To alleviate the problem of malnutrition, this paper describes a decision tree model for classification of infants being between the ages of 0 and 59 months as normal, acute malnourished or severely malnourished for three categories: Stunting, Wasting and Underweight. In decision tree model, Gini index is adopted as an impurity measure. The accuracy obtained using decision tree for stunting is 82.22%, for wasting 72.23 % and underweight 78.35% using Gini index.

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Dealing with claustrophobia using virtual reality

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AIP Conference Proceedings 2022年第1期2464卷

作者： R. Sharmikha Sree S. Meera R. Deepika R. A. Kalpana N. Ramya A. Ganga 1Department of Computer Science and Engineering SriSairam Engineering College West Tambaram Chennai - 600044 Tamil Nadu India

Claustrophobia - the fear of confined spaces is one of the most common phobias. It can be treated by many ways. Among all those, Virtual Reality Exposure Therapy (VRET) is the most widely used. The goal of this project is to treat Claustrophobia by VRET. It is done by placing the users in non-dangerous and user-friendly virtual environments. Those environments would provoke their Claustrophobic fear making them to overcome it. It is based on the ideology that the more you are exposed to what fears you, the less you will feel it. Different virtual environments like living room, bedroom, kitchen, restroom with various factors like tidiness and messiness, space wideness and narrowness, colour and brightness have been studied. The users can locomote inside the virtual environments by teleportation. It is achieved by gaze - based interactions which do not cause nausea so badly. The project is deployed as an app. The app can be viewed in a cheap, affordable and efficient way using Google Cardboard.

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Role of machine learning concepts in traffic flow prediction

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AIP Conference Proceedings 2022年第1期2426卷

作者： Nazirkar Reshma Ramchandra C. Rajabhushanam 1Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamilnadu India

Traffic congestion is defined as the state on the transport which is characterized by slow speeds, longer trips duration, and more number of vehicular queuing. It is more in urban areas than compared with rural areas. Traffic congestion occurs when the vehicles occupy greater space than the capacity of roads. Prediction of the traffic flow is useful for common people and vehicle users. Machine learning-based prediction systems are mainly used in various real-time application areas. This research work introduces machine learning concepts to predict traffic flow in an earlier manner. Here Deep Autoencoder (DAN), Deep Belief Network (DBN), and Random Forest (RF) applied on the online dataset for traffic flow prediction. Among the three techniques, RF produces a better outcome in terms of accuracy, precision, recall, RMSE, and MSE values. RF technique produces a 92.7% accuracy value.

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Feature reduction techniques for software bug prediction

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AIP Conference Proceedings 2022年第1期2576卷

作者： Tamanna O. P. Sangwan 1Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India

Developing a software entails writing thousands of lines of code. For ensuring quality of the software, this code must be fault free (should perform as it is intended to do). Software faults result in wastage of effort and resources used for developing it. Software bug prediction is a process in the initial period of Software Development Life Cycle (SDLC) which predicts bug-prone modules in a software. Various Machine Learning (ML) methods and feature reduction techniques have been employed for better fault prediction. In this paper six feature reduction techniques have been employed on five software bug datasets of AEEEM software repository in association with random forest-based ensemble classifier. SMOTE and Stratified 10-fold cross validation are used to improve performance of bug prediction model. Three performance metrics (ROC-AUC, F1-Score and accuracy) are ex-tracted for evaluating different dimensions of prediction. Feature agglomeration and Sparse principal component analysis performed better on these datasets for feature reduction.

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Classical Simulability of Quantum Circuits with Shallow Magic Depth

arXiv

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arXiv 2024年

作者： Zhang, Yifan Zhang, Yuxuan Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Physics Centre for Quantum Information and Quantum Control University of Toronto 60 Saint George Street TorontoONM5S 1A7 Canada Vector Institute Schwartz Reisman Innovation Campus W1140-108 College Street TorontoONM5G 0C6 Canada

Quantum magic is a necessary resource for for quantum computers to be not efficiently simulable by classical computers. Previous results have linked the amount of quantum magic, characterized by the number of T gates or stabilizer rank, to classical simulability. However, the effect of the distribution of quantum magic on the hardness of simulating a quantum circuit remains open. In this work, we investigate the classical simulability of quantum circuits with alternating Clifford and T layers across three tasks: amplitude estimation, sampling, and evaluating Pauli observables. In the case where all T gates are distributed in a single layer, performing amplitude estimation and sampling to multiplicative error are already classically intractable under reasonable assumptions, but Pauli observables are easy to evaluate. Surprisingly, with the addition of just one T gate layer or merely replacing all T gates with T 21 , the Pauli evaluation task reveals a sharp complexity transition from P to GapP-complete. Nevertheless, when the precision requirement is relaxed to 1/poly(n) additive error, we are able to give a polynomial time classical algorithm to compute amplitudes, Pauli observable, and sampling from log(n) sized marginal distribution for any magic-depth-one circuit that is decomposable into a product of diagonal gates. This rules out certain forms of quantum advantages in these circuits. Our research provides new techniques to simulate highly magical circuits while shedding light on their complexity and their significant dependence on the magic depth. © 2024, CC BY.

关键词： Quantum computers

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An intelligent traffic management system adapting image processing techniques in comparison with wireless sensor monitoring

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AIP Conference Proceedings 2022年第1期2519卷

作者： K. Anitha J. Aswini R. Sabitha M. Nalini 1Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu India 2Department of Computer Science and Engineering Sree Vidyanikethan Engineering College Tamilnadu India

Improving Economic growth of developing countries has promptedvarious vehicular movements in the town roads. Traffic management plays a huge part in regularizing vehicle movements and dodging blockings on roads. The pattern of traffic shall not be similar at all instants during the day and furthermore all the days. Huge traffics are constrained by traffic signals that allows and stops the vehicle movement for a pre-programmed time slots. This conventional method adopting the template have downsides. An intelligent electronic traffic controller can handle the traffic with sense consulting with the requirement at any instant of time. The paper briefs a Digital signal processor (controller) based smart traffic control system with the support of image processing techniques. The frameworkcapture the images from various roads meeting at a junction, recognizes the density of vehicles, analyzes between them, provide priorities, decides the dimensions of time slots for stop and permit and manages the traffic insightfully. Anextraordinarily mounted IR gadget on all the roads with the camera givesdata to the controller about vehicles proceeding onwardcrisis (such as ambulance) and deals with the traffic providing priority to such vehicles. The proposed framework was implemented on a proto mother board with TMS320 DSP processor, and the results are as expected, favorable and encouraging for implementing further developments.

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Breast cancer invasive carcinoma detection using deep convolution neural networks

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AIP Conference Proceedings 2022年第1期2464卷

作者： N. Ramya M. Shakthi Sree R. Srija R. A. Kalpana M. Kiruthiga Devi S. Meera S 1Department of Computer Science and Engineering Sri Sairam Engineering College West Tambaram Chennai Tamil Nadu India

Breast cancer is among the most common causes of cancer death in women across the *** ductal carcinomas account for nearly Eighty percent of all breast cancers. Invasive ductal carcinoma may impact women of any age that becomes more likely as women get older. Early diagnosis improves the chances of getting the correct way therapy and surviving, but it's a time-consuming procedure that can lead to pathologist disputes. computer-Aided Detection(CAD) systems have possibility for diagnosis of abnormalities and also improving accuracy. We devised a computational technique for classifying the histopathology cancer images using Deep CNN in this study. Eosin and Hematoxylin -Stained breast histology image dataset are used. Deep Neural Network Architectures and Random Forest classifiers are used.

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