 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- MABSearch: The Bandit Way of Learning the Learning Rate—A Harmony
Between Reinforcement Learning and Gradient Descent-
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Abstract: Gradient descent (GD) is an elementary optimization (OP) algorithm quite well-known in machine learning. It is the preferred choice of the OP algorithm for highly smooth and convex functions. In its native form, GD has one primary hyperparameter: Learning rate/Step size. Despite the availability of various methods, tuning this learning rate remains a considerable challenge. In this paper, we propose “MABSearch,” a GD algorithm using a multi-armed bandit (MAB) strategy to ‘learn’ and choose the optimal learning rate suitable for the objective function under consideration. The proposed algorithm remains true to its predecessors by preserving their innate simplicity. Python code of MABSearch is available at GitHub (https://github.com/Shahul-Rahman/MABSearch-Learning-the-learning-rate).
PubDate: 2023-06-04
- Security Aware Congestion Management Using Fuzzy Analytical Hierarchal
Process for Wireless Sensor Networks-
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Abstract: Congestion is one of the biggest problems faced by resource-constrained networks. Researchers have developed various congestion control strategies for sensor-based networks, but these strategies have mostly focused on the natural ways of congestion occurrence and neglected the possibility of malicious nodes intentionally creating congestion-like conditions. Moreover, Congestion occurs in WSNs for a variety of reasons such as resource constraints, many-to-one transmission manner, traffic bursts, packet forwarding and receiving rate mismatch, etc. This fact led to the development of a security attack-resistant congestion control mechanism that accounts for both potential sources of congestion in sensor nodes using multi-criteria decision-making (MCDM) approach as multiple factors are responsible for the existence of this undesired event. The proposed technique security aware congestion control using fuzzy AHP (SACC-FAHP) is an extension of the SACC-AHP technique. Since the AHP technique has a major limitation of subjective judgment. The fuzzy logic approach has improved the decision-making ability of basic AHP since it does not cover vagueness in personal judgments. Both techniques have been compared under the same simulation environment. According to the results of the simulation, SACC-FAHP produces better results than SACC-AHP.
PubDate: 2023-06-03
- Degree of Approximation of Functions, Conjugate to the Functions Belonging
to $$Lip((\xi _1,\xi _2); p)$$ -Class Through Double Matrix Means-
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Abstract: In this present work, we derive a theorem regarding the error of approximation of functions, conjugate to the functions of two variables ( \(2\pi\) -periodic in both variables) belonging to the generalized Lipschitz class \(Lip((\xi _1,\xi _2); p),p\ge 1\) through the double matrix means of their conjugate double Fourier series. Furthermore, in the form of corollaries, we present some particular cases of the theorem proved here.
PubDate: 2023-06-03
- Blister Beetle, Mylabris pustulata (Thunberg), Incidence on Tomato: A New
Report from Uttarakhand, India-
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Abstract: Tomato (Solanum lycopersicum L.) is an important and widely grown vegetable crop throughout India, and it has been attacked by several insect pests. This study sheds light on the infestation of Mylabris pustulata since its damage is noticed on tomato crop by feeding on buds, flowers, fruits, and sometimes even on tender leaves of tomato, either solitarily or gregariously. Further, the morphological and molecular identification (accession number-OP647113.1) carried out also confirms M. pustulata as a feeding agent. This is the new report on tomato crop infested by blister beetle, M. pustulata, observed in Lakhamandal village of Dehradun, Uttarakhand, India.
PubDate: 2023-06-02
- Prediction of Parent Data of Silkworm Breeding Based on Artificial Neural
Network-
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Abstract: A conventional breeding program typically deploys hundreds of crosses each year. However, only 1 or 2 percent of the combinations finally produce the desired variety. A large number of combinations are eliminated in the selection process of different breeding generations, breeding still depends largely on phenotypic selection and the experience of the breeder. The problems in traditional breeding, such as long cycles, low efficiency and poor foresight, have not been fundamentally solved. With the development of information technology and artificial intelligence, breeding with the help of modern technology has become a trend. The purpose of this study is to overcome the blindness of hybrid combinations by using computer artificial intelligence technology and establishing a simulated environment for silkworm breeding to help silkworm breeders. Based on backpropagation neural network, with the silkworm cocoon productivity as the breeding goal, the breeding model was established, the quantitative traits of more than 100 varieties were taken as samples to train the model, and the breeding simulation was carried out on this basis. By optimizing parental selection, and predicting parental selection, a hybridization test was carried out. The results showed that the difference between the predicted and tested values was 10.09% on average.
PubDate: 2023-06-01
- Ensemble of Time Series and Machine Learning Model for Forecasting
Volatility in Agricultural Prices-
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Abstract: Forecasting price volatility of agricultural commodities has immense importance nowadays. The use of traditional parametric model in capturing volatility in price series has been found to be inefficient. In this context, machine learning (ML) technique like support vector regression (SVR) may be applied to improve accuracy of forecasting. In the present investigation, an algorithm based on combination of parametric nonlinear time series model, i.e., generalized autoregressive conditional heteroscedastic (GARCH) model and supervised ML, e.g., SVR is proposed. The method is applied for forecasting volatility of onion price in two major markets of India, namely Delhi and Kolkata. The outperformance of the proposed algorithm in comparison to GARCH model has also been empirically established by means of Root Mean Square Error, Mean Absolute Error and R2 log.
PubDate: 2023-06-01
- Biology of Rose Beetle (Adoretus versutus) from Terai Foothills of Kumaon
Region in India-
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Abstract: Adoretus versutus (Harold) is a major species of white grub distributed across the Indian subcontinent. This work was conducted to study the life cycle of A. versutus in Terai foothills of Kumaon region. Laboratory research directed toward its different life stages was carried out by collecting its adults by installing light traps during their adult emergence. The experimental findings on its life cycle revealed that A. versutus completes its life cycle in an average time span of 245 days. It comprises of various stages viz: eggs—8 days, first instar—23 days, second instar—34 days, third instar—145 days, pupal period—10 days and adult stage—25 days. This study gives the significant information on the life stages of A. versutus which can be a foundational work to plan the effective management techniques in future cases of any outbreak.
PubDate: 2023-06-01
- PAPR Reduction of GFDM Signals Using Encoder-Decoder Neural Network
(Autoencoder)-
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Abstract: These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based method to lower the PAPR of GFDM. The PAPR-reducing network (PRNet), also known as the PAPR-reducing method, is based on the encoder-decoder neural network (Autoencoder). In the PAPR-reducing network (PRNet), the bit error rate (BER) and the PAPR of the GFDM system are jointly minimised by adaptively determining the constellation mapping and damping of symbols on each subcarrier and sub-symbol.
PubDate: 2023-06-01
- 3D Printed Meta-structure-Inspired Sensors of PVDF–Graphene–Mn-Doped
ZnO for Heritage Structures-
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Abstract: In the past decade, significant studies have been reported on 3D printed meta-structures for various structural, non-structural, and other engineering applications. But hitherto little has been reported on the use of 3D printed meta-structure-inspired sensors for online health monitoring of non-structural cracks in heritage buildings. This study reports the meta-structure 3D printing of polyvinylidene fluoride–6%graphene–3%Mn-doped ZnO composite (by weight proportion) as a smart solution for repair and online health monitoring (of repaired cracks) in heritage structures. Three meta-structures (tri-hexagonal, triangle, and octet) were explored for the proposed composition/proportion, and the mechanical, electrical, sensing and morphological properties were investigated. The results of the study outlined that the tri-hexagonal meta-structure has acceptable mechanical properties (strain hardening coefficient and stiffness in the composite matrix) along with electrical and sensing capabilities for the proposed application . The results obtained are also supported by scanning electron microscopy–energy-dispersive spectroscopy analysis.
PubDate: 2023-06-01
- A Bio-Inspired Technique for the Maximum Weighted Clique Problem
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Abstract: This paper discusses the particle swarm optimization-based approach, PSVC, for the maximum weighted clique problem (MWCP). This PSVC uses the swarm optimization technique with cost ratio parameter to construct the feasible solution for MWCP. This swarm technique is further tuned with two new local search procedures constructed with vertex tuning parameters that refine the feasible solution into the best optimal solution. The obtained results have been compared with the recently developed methods and statistical analysis made to show the effectiveness of the PSVC. These analyses indicate that the proposed PSVC is the best alternate approach for solving such NP-hard problems.
PubDate: 2023-06-01
- Impatiens parviflora DC. (Balsaminaceae): An Addition to the Indian Flora
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Abstract: Impatiens parviflora DC. (Small Balsam) is first time collected from Patnitop, Batote forest division in district Ramban, Union territory of Jammu and Kashmir, India. The species grows in moist and shady areas in pine forests between 2200 and 2500 m above msl. This is the first record of this species for India. The species is processed systematically and deposited in Herbarium, University of Jammu. Detailed taxonomic description of the species and key to species is worked out for the species based on peculiar morphological characters.
PubDate: 2023-06-01
- Experimental Investigations on the Effect of Textile Substrate
Nanomaterial Coating on Wearable Antenna Performance-
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Abstract: The variation in radiation characteristics of wearable antennas due to bending or folding of antenna during actual use is a major issue. The shift in resonant frequency values is most critical as it may lead to antenna operating in undesired frequency band. In this presented work, the dielectric nanomaterial coating on the textile substrate of the wearable antenna has been employed for the first time to control the effect of bending on wearable antenna performance. The experimental measurements show that the nanomaterial coating of the substrate results in smoother surface and filling up of airgaps in multilayer textile substrate leading to negligibly small shift in resonant frequency value in bent conditions, change in direction of shift and scope of miniaturization of wearable antenna.
PubDate: 2023-06-01
- Homotopy Analysis Method for Forced KdV Equation in Unmagnetized
Superthermal Plasmas-
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Abstract: The proposed work aims to show the advantage of the approximate analytical method for solving the Korteweg-de Vries (KdV) equation arising in superthermal plasma. The concept of electron acoustic solitary wave in an unmagnetised plasma consisting of superthermal electrons and periodic force has been considered. The standard KdV equation derived from the reductive perturbation technique has been considered and is evaluated by employing the homotopy analysis method (HAM). The approximate series solution obtained from HAM is compared with existing analytical and numerical results for the choice of auxiliary parameter values. The results indicate that the amplitude of solitary waves increases for increasing values of pertinent plasma parameters. This study demonstrates the potential and effectiveness of HAM to evaluate various kinds of nonlinear evolution equations arising in solitary wave theory.
PubDate: 2023-06-01
- Detecting True Medicinal Leaves Among Similar Leaves Using Computer Vision
and CNN-
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Abstract: Object detection and recognition have become integral components across various applications. Detecting desired objects of interest and analysing the same is used across several sophisticated applications like video surveillance, anomaly detectors, vehicle detection and tracking, person identification, etc. The same object recognition technique can be extended to analyse the images of medicinal leaves used in Siddha medicine and classify whether the right herbal leaf is picked for preparing medicine or therapy. This work focuses on developing a model that can detect and distinguish the right medicinal leaf from a look alike ordinary leaf using computer vision and machine learning. A leaf dataset was created that comprises of medicinal leaf and its look alike ordinary leaf. Computer vision techniques were used to extract features and pre-process the leaf images and the model uses Deep Convolution Neural Network to classify the right medicinal leaf from other look alike leaves. The proposed work has been tested with the dataset created and the results are shared.
PubDate: 2023-06-01
- Euaspis polynesia Vachal, 1903 (Hymenoptera: Apoidea: Megachilidae): A New
Addition to Bee Fauna of India with Comments on Natural History-
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Abstract: The genus Euaspis is known by three species from India viz. Euaspis carbonaria, Euaspis edentata, and Euaspis strandi. The present study reports on the occurrence of Euaspis polynesia Vachal, 1903 in India for the first time. The species has been recorded during a bee survey in Tawang district, Arunachal Pradesh, a high-altitude biodiversity hotspot region. Besides, this report also establishes the first record of the genus from the State. Diagnostic characters for taxonomic identification have been discussed with genitalia images. We present a summary and discussion regarding the floral associations and notes on the updated geographic distribution range of the four species of the genus Euaspis within India.
PubDate: 2023-06-01
- Accurate Dissolved Oxygen Prediction for Aquaculture Using Stacked
Ensemble Machine Learning Model-
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Abstract: Dissolved oxygen (DO) is the most vital water quality parameter that directly indicates the survival of aquatic life. Therefore, accurate DO prediction is essential for aquaculture water quality management for sustainable and profitable aquaculture production. Machine learning (ML) models have been successfully employed for water quality prediction. However, DO undergoes dynamic changes, which are nonlinear and complex, making accurate prediction of DO using conventional statistical methods and ML models a challenging task. To resolve this in this work, we propose a stacked ensemble ML model combining three different ML models as base learners and one ML model as a meta-learner to improve the DO prediction accuracy. The effectiveness of the stacked ensemble ML model has been evaluated using two different water quality datasets. The experimental results show that the stacked ensemble ML model achieves significant accuracy improvement compared with standalone ML models.
PubDate: 2023-06-01
- Screening of Nutrients for Enrichment of Extracellular Pullulanase
Production by Isolated Bacillus cereus KKSJ1981 Using Plackett–Burman
Design-
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Abstract: Current work intended to screen the different carbon, nitrogen sources and minerals which have a significant impact on the production of pullulanase by isolated Bacillus cereus KKSJ 1981 using Plackett–Burman design (PBD). Total 11 compounds (5 carbon sources, 3 nitrogen sources and 3 mineral salts) were screened by using 16 runs PBD. Each variable studied at 3 levels. The data were analyzed by first-order polynomial equation. The correlation coefficient was found to be 0.9876 and indicates the goodness of fit. Pareto chart of effects, normal probability plot and main effects plots were used to identify the significant variables and their level. It was observed that monosaccharide’s and dextran were found to be insignificant and remaining all variables found to be significant for production of pullulanase by isolated bacteria. Among all significant variables yeast extract has highest effect followed by soluble starch and MnSO4. This study reveals that yeast extract at low concentration, soluble starch and MnSO4 at higher level needed for the high titer of pullulanase production from B.cereus KKSJ 1981.
PubDate: 2023-06-01
- GLONASS-NavIC Hybrid Operation from India Towards Seamless and Improved
Performance-
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Abstract: The fully operational GLObal’naya NAvigatsionnaya Sputnikovaya Sistema (GLONASS) is used in many civilian and military applications as an alternative to Global Positioning System. However, it is observed that the GLONASS constellation regularly provides inferior satellite geometry resulting in higher Position Dilution of Precision (PDOP) values from various locations across the globe and fails to provide a seamless position solution in constrained visibility conditions. In such scenarios inclusion of the regional Navigation with Indian Constellation (NavIC) signals together with GLONASS can mitigate the problems and provide uninterrupted improved position solution accuracy from India. This paper presents the possible solution to the problem by including NavIC with GLONASS within the NavIC service region. Using real-time satellite data collected from eastern India in open-sky and constrained visibility environments, it is observed that the NavIC + GLONASS hybrid operation always offers good satellite geometry with PDOP values less than 3, resulting in uninterrupted position solution in satellite visibility constrained situations even up to 20–30° elevation, and improved position solution accuracy compared to GLONASS-only operation. GLONASS provides system independence while NavIC integration extends the benefits of improved satellite geometry and enhanced solution quality. The results of this study show the benefits of global–regional systems’ integrated operation for India and the surrounding regions both for defense and civilian applications.
PubDate: 2023-06-01
- Report on “The Second United Nations World Geospatial Information
Congress (UN-WGIC) Pre-Event”-
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Abstract: The United Nations Committee of Experts on Global Geospatial Information Management (GGIM) organises the World Geospatial Information Congress (UN-WGIC) once every four years bringing together member states of the UN-GGIM, experts in the field of Geospatial Technologies and its various application domains, commercial geospatial sector, academia, researchers and practitioners from across the globe. India is the proud host of the Second UN-WGIC scheduled for 10–14 October 2022 in Hyderabad, India. The Second UN-WGIC is organised by the United Nations and the Department of Science and Technology (DST), Government of India. Through a series of online virtual pre-events, DST intends to collect and collate various ideas and resource materials including applications and innovations in the field of Geospatial information generation to highlight the vibrant Indian Geospatial Ecosystem. One of the Pre events in India was held during 26th and 27th May, Co-ordinated by Prof. Biplab Biswas, Department of Geography, The University of Burdwan, Burdwan, West Bengal, India. The whole UN-WGIC pre-event was structured into—Inauguration; five technical sessions with presentations from various stakeholders like Government, Industry, Youth, Academics, others; and Conclusion with a detailed discussion over the nature, development and future trend of geospatial ecosystem in India and other countries.
PubDate: 2023-06-01
- Development of Ripening Gene-Specific Markers and their Association with
Shelf-Life in Mango (Mangifera indica L.) varieties-
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Abstract: Majority of the popular mango varieties have a very short shelf-life. Therefore, the present investigation was carried out to design functional markers and their association with shelf-life. A total of 35 shelf-life specific primers were designed using in silico mining of 95 ripening gene nucleotide sequences of Mangifera indica L. Of these specific primers, 27 showed polymorphism among the genotypes studied. Gene diversity (GD), average number of alleles per locus (An), polymorphism information content (PIC) and major allele frequency (Maf) observed were 0.38, 2.18, 0.30 and 0.68, respectively. Mango genotypes of varying shelf-life (long, medium and short) grouped separately into different clusters. Strong association of simple sequence repeats loci (SSRs) MSL-8 and MSLC-13 with physiological loss in weight (PLW) and titratable acidity was observed. Therefore, these ripening gene-specific SSRs loci could be used in marker-assisted breeding for fruit quality traits associated with shelf-life.
PubDate: 2023-06-01
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