Search results for: weed recognition

Authors: Daniel Kostrzewa, Henryk Josiński

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The expanded Invasive Weed Optimization algorithm (exIWO) is an optimization metaheuristic modelled on the original IWO version created by the researchers from the University of Tehran. The authors of the present paper have extended the exIWO algorithm introducing a set of both deterministic and non-deterministic strategies of individuals’ selection. The goal of the project was to evaluate the exIWO by testing its usefulness for solving some test instances of the traveling salesman problem (TSP) taken from the TSPLIB collection which allows comparing the experimental results with optimal values.

Keywords: expanded invasive weed optimization algorithm (exIWO), traveling salesman problem (TSP), heuristic approach, inversion operator

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: N. Viji, K. Siddeswaran

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Field experiment was conducted at Eastern Block farm, Department of Farm Management, Tamil Nadu Agricultural University, Coimbatore during winter season of 2011-2012 to evaluate potassium salt based glyphosate (Roundup Crop Shield 460 SL) with and without intercultural operations on seed cotton yield and weed nutrient removal in irrigated cotton. The experiment was laid out in Randomized Block Design with treatments replicated thrice. The treatments consisted of POE glyphosate (Roundup Crop Shield 460 SL) at 1350 (T1), 1800 (T2), 2250 (T3) g a.e. ha-1, 1800 g a.e. ha-1 + IC (T4), PE pendimethalin at 750 g a.i. ha-1 + IC (T5), HW at 35 and 70 DAS + IC (T6), HWW at 35 and 70 DAS + IC (T7), PWW at 35 and 70 DAS + IC (T8), HW at 25 and 45 DAS (T9) and Unweeded control (T10). Among the weed management methods, decreased nutrient removal by weeds were observed with POE glyphosate at 1800 g a.e. ha-1 + IC which was comparable with PE pendimethalin at 750 g a.i. ha-1 + IC. Higher seed cotton yield was obtained with POE glyphosate at 1800 g a.e. ha-1 at 35 and 70 DAS with + IC at 45 and 55 DAS which was comparable with PE pendimethalin at 750 g a.i. ha-1 + IC at 45 and 55 DAS. Comparing treatments without intercultural operation, intercultural operation carried out treatments performed better and recorded more seed cotton yield.

Keywords: cotton, weed, glyphosate, nutrient

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Ijaz Ahmad

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Weed competition is one of the major biotic constraints in wheat crop productivity. Avena fatua L. and Silybum marianum (L.) Gaertn. are among the worst weeds of wheat, greatly deteriorating wheat quality subsequently reducing its market value. In this connection, two-year experiments were conducted in 2018 & 2019. Different seeding rate wheat viz; 80, 100, 120 and 140 kg ha-1 and different weeds ratio (A. fatua: S. marianum ) sown at the rate 1:8, 2:7, 3:6, 4:5, 5:4, 6:3, 7:2, 8:1 and 0:0 respectively. The weeds ratio and wheat densities are indirectly proportional. However, the wheat seed at the rate of 140 kg ha-1 has minimal weeds interference. Yield losses were 17.5% at weeds density 1:8 while 7.2% at 8:1. However, in wheat density, the highest percent losses were computed on 80 kg ha-1 while the lowest was recorded on 140 kg ha-1. Since due to the large leaf canopy of S. marianum other species can't sustain their growth. Hence, it has been concluded that S. marianum is the hotspot that causes reduction to the yield-related parameters, followed by A. fatua and the other weeds. Due to the morphological mimicry of A. fatua with wheat crop during the vegetative growth stage, it cannot be easily distinguished. Therefore, managing A. fatua and S. marianum before seed setting is recommended for reducing the future weed problem. Based on current studies, it is suggested that sowing wheat seed at the rate of 140 kg ha-1 is recommended to better compete with all the field weeds.

Keywords: fat content, holly thistle, protein content, weed competition, wheat, wild oat

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Authors: Van Nhan Nguyen, Harald Holone

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Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: automatic speech recognition, asr, air traffic control, atc

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Authors: Ying Chen

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Conservation Agriculture in a sustainable way of farming, as it brings many benefits, such as preventing soil from erosion and degradation, improving soil health, conserving energy, and sequestrating carbon. However, adoption of conservation agriculture has been progressing slowly in some part of the world due to some challenges. Among them, seeding in heavy crop residue is challenging, especially in corn production systems. Weed control is also challenging in conservation agriculture. This research aimed to investigate some technologies that can address these challenges. For crop residue management, vertical tillage and vertical seeding have been studied in multiple research projects. Results showed that vertical tillage and seeding were able to deal with crop residue through cutting residue into small segments, which would not plug seeder in the sub-sequent seeding. Vertical tillage is a conservation tillage system, as it leaves more than 30% crop residue on soil surface while incorporating some residue into the shallow soil layer for fast residue decomposition. For weed control, mechanical weeding can reduce chemical inputs in crop production. A tine weeder was studied for weed control during the early growing season of several field crops (corn, soybean, flax, and pea). Detail results of these studies will be shared at the conference.

Keywords: tillage, seeding, mechanical weeding, crop residue

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Ainagul Yermekova, Liudmila Goncharenko, Ali Baghirzade, Sergey Sybachin

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In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc.

Keywords: text detection, template method, recognition algorithm, structured method, feature method

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Authors: Faezeh Zaefarian1, Sajedeh Golmohammadzadeh, Mohammad Rezvani

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Weed management decisions for weed species can be derived from knowledge of seed germination biology. Experiments were conducted in laboratory and greenhouse to determine the effects of light, temperature, salt and water stress, seed burial depth on seed germination and seedling emergence of Papaver rhoeas and P.dubium and to assay the response of these species to commonly available POST herbicides. Germination of the Papaver seeds was influenced by the tested temperatures (day/night temperatures of 20 and 25 °C) and light. The concentrations of sodium chloride, ranging from 0 to 80 mM, influence germination of seeds. The osmotic potential required for 50% inhibition of maximum germination of P. rhoeas was -0.27 MPa and for P. dubium species was 0.25 MPa. Seedling emergence was greatest for the seeds placed at 1 cm and emergence declined with increased burial depth in the soil. No seedlings emerged from a burial depth of 6 cm. The herbicide 2,4-D at 400 g ai ha-1 provided excellent control of both species when applied at the four-leaf and six-leaf stages. However, at the six-leaf stage, percent control was reduced. The information gained from this study could contribute to developing components of integrated weed management strategies for Papaver species.

Keywords: germination, papaver species, planting depth, POST herbicides

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Muhammad Haroon, LI Xiangju

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C. communis L. is an important weed of many crop, but very little information about the adaptation strategies of C. communis L. biotypes under drought stress. We investigated five biotypes of C. communis L under drought stress to identify the adaptation mechanism. The expression of drought stress related genes (DRS1, EREB and HRB1) was up-regulated in biotypes, while in some biotypes their expression was down regulated. All five biotypes can thus regulate water balance to consume less water to maintain their status under drought stress condition. This result concluded that C. communis L. biotypes can survive longer under drought stress condition. Weed scientist should seek more effective management strategies to deal with C. communis L.

Keywords: C. communis, biotypes, drought stress, gene expression

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Sevil Igit, Merve Meric, Sarp Erturk

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In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach.

Keywords: face recognition, Daisy descriptor, One-Bit Transform, image registration

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Authors: S. Jagadeesh Kumar

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This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

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Authors: Shabir Ahmad Sofi, Shubham Aggarwal, Sanyam Singhal, Roohie Naaz

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Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. These features are used to provide an authentication for computer based security systems. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on 'who she/he is' rather than 'what she/he has' (card, token, key) or 'what she/he knows' (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal and their advantages and disadvantages, will be presented.

Keywords: biometric, DNA, fingerprint, ear, face, retina scan, gait, iris, voice recognition, unimodal biometric, multimodal biometric

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Authors: Roihatul Mutiah, Sukardiman, Aty Widyawaruyanti

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Calotropis gigantiea (L.) R. Br (Apocynaceae) commonly called as “Biduri” or “giant milk weed” is a well-known weed to many cultures for treating various disorders. Several studies reported that C.gigantea roots has anticancer activity. The main aim of this research was to isolate and identify an active marker compound of C.gigantea roots for quality control purpose of its extract in the development as anticancer natural product. The isolation methods was bioactivity guided column chromatography, TLC, and HPLC. Evaluated anticancer activity of there substances using MTT assay methods. Identification structure active compound by UV, 1HNMR, 13CNMR, HMBC, HMQC spectral and other references. The result showed that the marker active compound was identical as Calotropin.

Keywords: calotropin, Calotropis gigantea, anticancer, marker active

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Authors: Phawin Sangsuvan, Chutimet Srinilta

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This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function.

Keywords: character recognition, histogram projection, particle swarm optimization, pattern recognition techniques

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Authors: Benjawan Rangsikamol, Chutimet Srinilta

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This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

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Authors: F. Lops, G. Disciglio, A. Carlucci, G. Gatta, L. Frabboni, A. Tarantino, E. Tarantino

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Phelipanche ramosa L. Pomel is a root holoparasitic weed plant of many cultivations, particularly of tomato (Lycopersicum esculentum L.) crop. In Italy, Phelipanche problem is increasing, both in density and in acreage. The biological control of this parasitic weed involves the use of living organisms as numerous fungi and bacteria that can infect the parasitic weed, while it may improve the crop growth. This paper deals with the biocontrol with microorganism, including Arbuscular mycorrhizal (AM) fungi and fungal pathogens as Fusarium oxisporum spp. Colonization of crop roots by AM fungi can provide protection of crops against parasitic weeds because of a reduction in their seed germination and attachment, while F. oxisporum, isolated from diseased broomrape tubercles, proved to be highly virulent on P. ramosa. The experimental trial was carried out in open field at Foggia province (Apulia Region, Southern Italy), during the spring-summer season 2016, in order to evaluate the effect of four biological treatments: AM fungi and Fusarium oxisporum applied in the soil alone or combined together, and Rizosum Max^® product, compared with the untreated control, to reduce the P. ramosa infestation in processing tomato crop. The principal results to be drawn from this study under field condition, in contrast of those reported previously under laboratory and greenhouse conditions, show that both AM fungi and F. oxisporum do not provide the reduction of the number of emerged shoots of P. ramosa. This can arise probably from the low efficacy seedling of the agent pathogens for the control of this parasite in the field. On the contrary, the Rizosum Max^® product, containing AM fungi and some rizophere bacteria combined with several minerals and organic substances, appears to be most effective for the reduction of P. ramosa infestation.

Keywords: Arbuscular mycorrhized fungi, biocontrol methods, Phelipanche ramosa, tomato crop

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Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

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This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: extreme learning, LIRA neural classifier, speaker identification, voice recognition

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Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby, Anders Krogh Mortensen, Sanmohan Baby

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This work contributes a statistical model and simulation framework yielding the best estimate possible for the potential herbicide reduction when using the MoDiCoVi algorithm all the while requiring a efficacy comparable to conventional spraying. In June 2013 a maize field located in Denmark were seeded. The field was divided into parcels which was assigned to one of two main groups: 1) Control, consisting of subgroups of no spray and full dose spraty; 2) MoDiCoVi algorithm subdivided into five different leaf cover thresholds for spray activation. In addition approximately 25% of the parcels were seeded with additional weeds perpendicular to the maize rows. In total 299 parcels were randomly assigned with the 28 different treatment combinations. In the statistical analysis, bootstrapping was used for balancing the number of replicates. The achieved potential herbicide savings was found to be 70% to 95% depending on the initial weed coverage. However additional field trials covering more seasons and locations are needed to verify the generalisation of these results. There is a potential for further herbicide savings as the time interval between the first and second spraying session was not long enough for the weeds to turn yellow, instead they only stagnated in growth.

Keywords: herbicide reduction, macrosprayer, weed crop discrimination, site-specific, sprayer boom

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Authors: U. Ravi Babu, Mohd Mastan

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The present paper proposes a novel approach for handwritten digit recognition system. The present paper extract digit image features based on distance measure and derives an algorithm to classify the digit images. The distance measure can be performing on the thinned image. Thinning is the one of the preprocessing technique in image processing. The present paper mainly concentrated on an extraction of features from digit image for effective recognition of the numeral. To find the effectiveness of the proposed method tested on MNIST database, CENPARMI, CEDAR, and newly collected data. The proposed method is implemented on more than one lakh digit images and it gets good comparative recognition results. The percentage of the recognition is achieved about 97.32%.

Keywords: handwritten digit recognition, distance measure, MNIST database, image features

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Authors: Faizan Tariq, Moayid Ali Zaidi

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Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction.

Keywords: face recognition, emotion recognition, deep learning, CNN

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: binary vector quantization (BVQ), DCT coefficients, face recognition, local binary patterns (LBP)

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: Seada Hussen Adem, Frie Ayalew Yimam

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Due to its high biomass output and potential to produce renewable energy, water hyacinth, a rapidly expanding aquatic weed, has gained recognition as a prospective bioenergy feedstock. Through a variety of conversion processes, such as anaerobic digestion, combustion, and gasification, this study suggests using water hyacinth to generate energy. The suggested strategy helps to reduce the annoyance brought on by the excessive growth of water hyacinth in Tana water bodies in addition to offering an alternate source of energy. The study emphasizes the value of environmentally friendly methods for managing Tana water resources as well as the potential of water hyacinth as a source of bioenergy.

Keywords: anaerobic digestion, bioenergy, combustion, gasification, water hyacinth

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Norhafilah Bara

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Developing a software application using an interface with computers and peripheral devices using gestures of human body such as hand movements keeps growing in interest. A review on this hand gesture detection and recognition based on computer vision technique remains a very challenging task. This is to provide more natural, innovative and sophisticated way of non-verbal communication, such as sign language, in human computer interaction. Nevertheless, this paper explores hand detection and hand gesture recognition applying a vision based approach. The hand detection and recognition used skin color spaces such as HSV and YCrCb are applied. However, there are limitations that are needed to be considered. Almost all of skin color space models are sensitive to quickly changing or mixed lighting circumstances. There are certain restrictions in order for the hand recognition to give better results such as the distance of user’s hand to the webcam and the posture and size of the hand.

Keywords: hand detection, hand gesture, hand recognition, sign language

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