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Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Carbon dots derived from frankincense soot for ratiometric and colorimetric detection of lead (II)
We report a simple one-pot hydrothermal synthesis of carbon dots from frankincense soot. Carbon dots prepared from frankincense (FI-CDs) have narrow size distribution with an average size of 1.80 nm. FI-CDs emit intense blue fluorescence without additional surface functionalization or modification. A negative surface charge was observed for FI-CDs, indicating the abundance of epoxy, carboxylic acid, and hydroxyl functionalities that accounts for their stability. A theoretical investigation of the FI-CDs attached to oxygen-rich functional groups is incorporated in this study. The characteristics of FI-CDs signify arm-chair orientation, which is confirmed by comparing the indirect bandgap of FI-CDs with the bandgap obtained from Tauc plots. Also, we demonstrate that the FI-CDs are promising fluoroprobes for the ratiometric detection of Pb2+ ions (detection limit of 0.12 ?M). The addition of Pb2+ to FI-CD solution quenched the fluorescence intensity, which is observable under illumination by UV light LED chips. We demonstrate a smartphone-assisted quantification of the fluorescence intensity change providing an efficient strategy for the colorimetric sensing of Pb2+ in real-life samples. 2022 IOP Publishing Ltd. -
Amine functionalized carbon quantum dots from paper precursors for selective binding and fluorescent labelling applications
We report a novel synthesis route for preparing carbon quantum dots (CQDs) of customized surface functionality from readily available precursors. The synthetic strategy is based on the chemical modification of paper precursors prior to preparing CQDs from them. The pre-synthesis modification of paper precursors with (3-Aminopropyl) triethoxy silane (APTES) enabled us to synthesize CQDs with amine functional groups on the surface. The silane coupling via condensation between the ethoxy group of APTES and the cellulose hydroxyl group on the paper resulted in the tethering of amine groups on the paper substrates, which are retained as surface-bound species during the synthesis of CQDs from the modified paper. Amine functionalization on the surface of CQDs helped us use them in applications such as DNA binding. We analyzed the interaction of CQDs with calf thymus DNA (CT-DNA), and the results imply their propensity as an efficient biological probe. The synthetic strategy presented here can also be extended to other functional groups. 2022 Elsevier Inc. -
Further Discussion on the Significance of Quartic Autocatalysis on the Dynamics of Water Conveying 47nm Alumina and 29nm Cupric Nanoparticles
Improvement of product performance, efficiency, and reliability is a major concern of experts, scientists, and technologists dealing with the dynamics of water conveying nanoparticles on objects with nonuniform thickness either coated or sprayed with the catalyst. However, little is known on the significance of quartic autocatalysis as it affects the dynamics of water conveying alumina and cupric nanoparticles. In this study, comparative analysis between the dynamics of water conveying 29nm CuO and 47nm Al 2O 3 on an upper horizontal surface of a paraboloid of revolution is modeled and presented. In the transport phenomena, migration of nanoparticles due to temperature gradient, the haphazard motion of nanoparticles, and diffusion of motile microorganisms were incorporated into the mathematical models. Due to the inherent nature of the thermophysical properties of the two nanofluids, viscosity, density, thermal radiation, and heat capacity of the two nanofluids were incorporated in the mathematical model. The nonlinear partial differential equations that model the transport phenomenon were transformed, non-dimensionalized and parameterized. The corresponding boundary value problems were converted to an initial value problem using the method of superposition and solved numerically. The concentration of the catalyst increases significantly with buoyancy at a larger magnitude of space-dependent internal heat source in the flow of 29nm CuOwater nanofluid. Negligible migration of nanoparticles due to temperature gradient decreases the concentration of the fluid throughout the domain. 2020, King Fahd University of Petroleum & Minerals. -
Ochratoxin A as an alarming health threat for livestock and human: A review on molecular interactions, mechanism of toxicity, detection, detoxification, and dietary prophylaxis
Ochratoxin A (OTA) is a toxic metabolite produced by Aspergillus and Penicillium fungi commonly found in raw plant sources and other feeds. This review comprises an extensive evaluation of the origin and proprieties of OTA, toxicokinetics, biotransformation, and toxicodynamics of ochratoxins. In in vitro and in vivo studies, the compatibility of OTA with oxidative stress is observed through the production of free radicals, resulting in genotoxicity and carcinogenicity. The OTA leads to nephrotoxicity as the chief target organ is the kidney. Other OTA excretion and absorption rates are observed, and the routes of elimination include faeces, urine, and breast milk. The alternations in the Phe moiety of OTA are the precursor for the amino acid alternation, bringing about Phe-hydroxylase and Phe-tRNA synthase, resulting in the complete dysfunction of cellular metabolism. Biodetoxification using specific microorganisms decreased the DNA damage, lipid peroxidation, and cytotoxicity. This review addressed the ability of antioxidants and the dietary components as prophylactic measures to encounter toxicity and demonstrated their capability to counteract the chronic exposure through supplementation as feed additives. 2022 Elsevier Ltd -
Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm
The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE. -
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Bioconversion of chicken feather waste into feather hydrolysate by multifaceted keratinolytic Bacillus tropicus LS27 and new insights into its antioxidant and plant growth-promoting properties
Abstract: Keratin, the main structural constituent of feathers, contains a lot of valuable amino acids which are potential bioactive compounds as well. Since conventional methods are not efficient enough to achieve complete removal of chicken feather waste, biological mode of feather degradation is one of the most appropriate ways to utilize feathers, thereby reducing wastes as well as generating value-added products from feathers. This study was focussed on valorizing chicken feather into feather hydrolysate (FH) containing bioactive compounds for plant growth promotion. Keratinolytic bacteria capable of degrading chicken feathers were isolated from the poultry waste dumping site of Russell Market, Shivajinagar, Bangalore, Karnataka, India. The isolated bacteria was identified as Bacillus tropicus LS 27. A minimal media with chicken feather as the sole source of carbon and nitrogen was prepared and inoculated with Bacillus tropicus LS 27 [5% (v/v)]. Degradation of keratin protein by bacteria caused the solubilization of amino acids which was confirmed by high-performance liquid chromatography (HPLC) analysis where an appreciable amount of amino acids like cysteine, valine, isoleucine, proline, lysine, methionine, and phenylalanine was detected. The Fourier transform infrared spectroscopy (FTIR) analysis of hydrolysed chicken feathers showed C=0 stretching, S-H bond stretching, and formation of carboxylic acid groups indicating effective degradation of chicken feathers. Scanning electron microscope (SEM) images revealed the degradation pattern of feathers showing complete degradation of barbs and barbules with a portion of rachis remaining. Feather hydrolysate was further explored for its antioxidant activity using DPPH scavenging assay, and the value was found to be 1.5 mg/mL. The bacterial cells when screened for heavy metal tolerance showed significant metal tolerance to lead (Pb) and chromium (Cr). Since Bacillus tropicus LS27 showed indole-3-acetic acid (IAA), siderophore, and ammonia production, the prepared feather hydrolysate along with the bacterial cells were used as soil amendment for plant growth studies over Spinacia oleracea L. The study revealed that plants supplemented with 20% (v/v) FH showed elevated plant growth, therefore proving to be optimum for the support of plant growth. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Optimized production of keratinolytic proteases from Bacillus tropicus LS27 and its application as a sustainable alternative for dehairing, destaining and metal recovery
The present study describes the isolation and characterization of Bacillus tropicus LS27 capable of keratinolytic protease production from Russell Market, Shivajinagar, Bangalore, Karnataka, with its diverse application. The ability of this strain to hydrolyze chicken feathers and skim milk was used to assess its keratinolytic and proteolytic properties. The strain identification was done using biochemical and molecular characterization using the 16S rRNA sequencing method. Further a sequential and systematic optimization of the factors affecting the keratinase production was done by initially sorting out the most influential factors (NaCl concentration, pH, inoculum level and incubation period in this study) through one factor at a time approach followed by central composite design based response surface methodology to enhance the keratinase production. Under optimized levels of NaCl (0.55 g/L), pH (7.35), inoculum level (5%) and incubation period (84 h), the keratinase production was enhanced from 41.62 U/mL to 401.67 9.23 U/mL (9.65 fold increase) that corresponds to a feather degradation of 32.67 1.36% was achieved. With regard to the cost effectiveness of application studies, the crude enzyme extracted from the optimized medium was tested for its potential dehairing, destaining and metal recovery properties. Complete dehairing was achieved within 48 h of treatment with crude enzyme without any visible damage to the collagen layer of goat skin. In destaining studies, combination of crude enzyme and detergent solution [1 mL detergent solution (5 mg/mL) and 1 mL crude enzyme] was found to be most effective in removing blood stains from cotton cloth. Silver recovery from used X-ray films was achieved within 6 min of treatment with crude enzyme maintained at 40 C. 2023 Elsevier Inc. -
STABILITY IN CHAOS: IMPACT OF MONETARY, FISCAL, AND FIRM CHARACTERISTICS ON INVESTOR SENTIMENT IN ASIAN EMERGING MARKETS
This study investigates the impact of firm characteristics, monetary policies, and fiscal policies on investor sentiment, specifically focusing on market volatility and trading volume in six Asian emerging markets during the pre-pandemic and pandemic periods. Using panel data regression on a sample of 5,619 firms between 2015 and 2023, this study analyses the distinct roles of firm-specific factors and macroeconomic policies in shaping market behaviour during periods of economic instability. The findings reveal that firm characteristics such as capital structure and payout policies consistently drive both volatility and trading volume. Monetary policies, particularly interest rates and money supply, showed heightened significance during the pandemic, while fiscal policies, though largely insignificant pre-pandemic, became more relevant during the crisis. The study's results provide critical insights for policymakers and investors on the dynamic interplay between firm-level and macroeconomic factors during crisis periods, emphasising the need for coordinated policy responses. 2024, Universiti Malaysia Sarawak. All rights reserved. -
Exploratory analysis of legal case citation data using node embedding
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019. -
Heat and Mass Transport in Casson Nanofluid Flow over a 3-D Riga Plate with Cattaneo-Christov Double Flux: A Computational Modeling through Analytical Method
This work examines the non-Newtonian Cassonnanofluids three-dimensional flow and heat and mass transmission properties over a Riga plate. The Buongiorno nanofluid model, which is included in the present model, includes thermo-migration and random movement of nanoparticles. It also took into account the CattaneoChristov double flux processes in the mass and heat equations. The non-Newtonian Casson fluid model and the boundary layer approximation are included in the modeling of nonlinear partial differential systems. The homotopy technique was used to analytically solve the systems governing equations. To examine the impact of dimensionless parameters on velocities, concentrations, temperatures, local Nusselt number, skin friction, and local Sherwood number, a parametric analysis was carried out. The velocity profile is augmented in this study as the size of the modified Hartmann number increases. The greater thermal radiative enhances the heat transport rate. When the mass relaxation parameter is used, the mass flux values start to decrease. 2023 by the authors. -
Assembly of discrete and oligomeric structures of organotin double-decker silsesquioxanes: Inherent stability studies
Double-decker silsesquioxane (DDSQ), a type of incompletely condensed silsesquioxane, has been used as a molecular precursor for synthesizing new organotin discrete and oligomeric compounds. The equimolar reaction between DDSQ tetrasilanol (DDSQ-4OH) and Ph2SnCl2 in the presence of triethylamine leads to obtaining discrete [Ph4Sn2O4(DDSQ)(THF)2] (1). The change of sterically bulky aryl Ph2SnCl2 precursor to linear alkyl nBu2SnCl2 led to the isolation of oligomeric [nBu4Sn2O4(DDSQ)] (2). The structures of compounds 1 and 2 have been demonstrated using single-crystal X-ray diffraction measurements. Indeed, the formation of oligomeric organotin DDSQ compound (2) was determined using GPC and MALDI-TOF mass spectroscopy. In compound 1, the geometry of the tin atom is five-coordinated trigonal bipyramidal by two phenyl groups, two Si-O from DDSQ and one tetrahydrofuran. Compound 2 contains four coordinated two peripheral tin atoms and two five-coordinated central tin atoms, in which, the fifth coordinating oxo groups in the central tin atoms create the bridge between two different DDSQ units that leads to the formation of oligomeric structure. Density functional theory calculations on organotin DDSQs infer that the obtained lattice energy for compound 1 is far higher than for the case of compound 2, which indicates that the crystal of compound 1 is better stabilized in its crystal lattice with stronger close packing via intermolecular interactions between discrete molecules with coordinated THF compared to the crystal of compound 2. The greater stability arises mainly due to the sterically bulkier phenyl groups attached to the tin centers in compound 1, which provide accessibility for accommodating the THF molecule per tin via Sn-THF bonding, while contrarily the smaller n-butyl groups aid the polymerization of the four repeating units of [SnSi4O7] or two Sn2O4(DDSQ) through ?-oxo groups. Both compounds 1 and 2 were chosen to be promising precursors for the synthesis of ceramic tin silicates. The thermolysis of 2 at 1000 C afforded the mixture of crystalline SnSiO4 and SiO2 but the same mixture was only formed by thermolysis of 1 at relatively higher temperature (1500 C), which infers that compound 1 is more stable than compound 2 that is in good synergy with theoretical lattice energy. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
A site-isolated Lewis acidic aluminium and Brsted basic amine sites in the dimeric silsesquioxane cage as a reusable homogeneous bifunctional catalyst for one-pot tandem deacetalization/deketalization-Knoevenagel condensation reactions
The development of multifunctional catalysts for one-pot tandem reactions is significantly required to attain multiple sequential transformations in a single reactor, which would considerably decrease the number of manipulations demanded for chemical manufacturing in industries. Herein, dimeric silsesquioxane Al-POSS-NH2 (2), a homogenous bifunctional acid-base catalyst containing environmentally friendly robust silica and high chemical and thermal stabilities, permanent catalytic activity, and reusability, was synthesized by the reaction of trisilanol aminopropyl hexaisobutyl-POSS (1) with trimethylaluminium. Al-POSS-NH2 was successfully used as a bifunctional catalyst for one-pot tandem reactions because of the synergism and effective compartmentalization between Lewis acidic aluminium and Brsted basic amine sites (>10.0 in the dimeric silsesquioxane cage, which was confirmed by DFT and QTAIM studies. Subsequently, different acetals were tested to obtain their corresponding benzylidene malononitrile derivatives using Al-POSS-NH2 for the one-pot tandem deacetalization-Knoevenagel condensation reactions and showed high efficiency (>90%) under optimized conditions (DMF, 0.3 mol% catalyst loading and 80 C) with different reaction times. Furthermore, the bifunctional Al-POSS-NH2 catalyst was separated from the reaction mixture via the precipitation method by adding acetonitrile into the reaction mixture and reusing it for five consecutive cycles without losing activity considerably, thus providing the inherent advantage over traditional homogeneous catalysts. In a one-pot tandem deketalization-Knoevenagel condensation reaction for various ketals, the reaction condition was slightly modified by increasing the catalyst loading (0.6 mol%) and reaction time (16 to 24 hours) to acquire better conversion and yield of their desired products. Finally, the present study suggests that the bifunctional POSS might facilitate the rapid development of environmentally friendly and economically feasible catalysts for multistep reactions. 2023 The Royal Society of Chemistry. -
In situ growth of octa-phenyl polyhedral oligomeric silsesquioxane nanocages over fluorinated graphene nanosheets: super-wetting coatings for oil and organic sorption
Superhydrophobic surfaces offer significant advantages through their hierarchical micro/nanostructures, which create optimal surface roughness and low surface energy, making the development of robust surfaces essential for enhancing their physical and chemical stability. Here, we introduce in situ growth of octa-phenyl polyhedral oligomeric silsesquioxane (O-Ph-POSS) nanocages over multi-layered fluorinated graphene (FG) nanosheets through hydrolysis/condensation of phenyl triethoxysilane in an alkaline medium to produce a robust POSS-FG superhydrophobic hybrid. The efficient in situ growth of O-Ph-POSS nanocages over FG nanosheets was confirmed by FT-IR spectroscopy, PXRD, SEM, TEM, TG analysis, 29Si NMR spectroscopy, N2 adsorption-desorption isotherms and XP spectroscopy. The as-synthesized O-Ph-POSS over FG becomes superhydrophobic with a water contact angle (WCA) of 152 2 and a surface free energy (SFE) of 5.6 mJ m?2. As a result of the superhydrophobic property and robust nature of the POSS nanocage, O-Ph-POSS over FG nanosheets revealed the absorption capability for oils/organic solvents ranging from 200 to 500 wt% and were applied to coat onto the polyurethane (PU) sponge to effectively separate various oils and organic solvents from water mixtures, achieving separation efficiencies between 90% and 99%. Importantly, O-Ph-POSS-FG@Sponge still retained a separation efficiency of over 95% even after 25 separation cycles for hexane spill in water. The sponge efficiently separates toluene and chloroform using a vacuum pump, achieving flux rates of up to 20 880 and 12 184 L m?2 h?1, respectively. Weather resistance tests of O-Ph-POSS-FG@Sponge, prepared at intervals of 1 week and 1 year, showed that aged samples retained similar WCA values to freshly prepared sponges, confirming their long-term durability and performance. Mechanical stability assessments indicated that O-Ph-POSS-FG@Sponge maintained superhydrophobic properties, with WCA values of 151 2 for tape peeling and emery paper treatments and 150 2 for knife cutting, highlighting its excellent stability under physical deformation. Additionally, leveraging the exceptional resistance of O-Ph-POSS, the superhydrophobic O-Ph-POSS-FG@Sponge exhibited excellent stability and durability, even under supercooled and hot conditions during oil/water separation. Optical microscopy analysis of O/W and W/O emulsions, both stabilized by a surfactant, revealed complete droplet separation, further confirming the O-Ph-POSS-FG@Sponge's effectiveness for emulsion separation applications. The present work provides a straightforward method for the large-scale production of robust, superhydrophobic materials suitable for cleaning up oil spills on water surfaces. 2025 The Royal Society of Chemistry. -
IndiaEuropean Union Trade Integration: An Analysis of Current and Future Trajectories
In a dynamic global environment of increased economic interdependence, nations are more than ever seeking to remove barriers to trade, despite growing trends of protectionism. In this context, India and the EU-27 have initiated talks for the establishment of a Bilateral Trade and Investment Agreement (BTIA) in an attempt to bring their economies together. However, after 16 rounds of negotiations, the failure to conclude this agreement has raised questions regarding the benefits of the agreement to India. This study attempts to examine the current trade scenario and the effects of the proposed regional trade agreement by estimating a structural gravity model. This study employs the Poisson Pseudo Maximum Likelihood (PPML) estimator for analysing the trade-creation and trade-diversion effects of the BTIA to overcome the shortcomings of ordinary least square (OLS) estimators. For the empirical analysis, the merchandise export data from the Gravity database has been taken for a period of 19 years from 2001 to 2019. The results indicate that the BTIA could lead to trade creation and trade diversion, highlighting the need for a re-evaluation of Indias trade policy. JEL Classification: F10, F13, F14, F15, O24 2021 National Council of Applied Economic Research. -
Analysis of indias trade patterns and trade possibilities with the european union
Trade has played a crucial role in the emergence of developing econo-mies. The global emergence of India is also linked to its role in global trade. In this context, the European Union and India initiated talks for a free trade agreement known as the Bilateral Trade and Investment Agreement (BTIA). However, this agreement has failed to materialise due to various challenges and disputes. Against this backdrop, the present study attempts to trace the existing pattern of trade relations between India and the EU and provide a preliminary analysis of the nature of trade in this proposed region. A modified gravity equation and indicators of regional trade interdependence have been estimat-ed. The results indicate that trade within this region is in line with cer-tain predictions of the gravity model. Additionally, it also indicates that such an agreement has little potential for expanding trade and might even result in unnatural trade. Thus, it provides evidence for the argu-ment that India can benefit from developing ties with similar emerging economies in the Asia-Pacific neighbourhood. 2020, WSB University. All rights reserved. -
The feasibility analysis of load based resource optimization algorithm for cooperative communication in 5G wireless ad-hoc networks
Efficient allocation of resources is crucial in wireless ad hoc networks (WANETs) as spectrum assets are costly. Cooperative communications were introduced as a solution to the problem of limited spectrum availability. In this approach, numerous nodes share their resources and increase the bandwidth available to end-users. This research investigates the practicality of a new algorithm that optimizes resources based on load for Cooperative Communications in 5 G WANETs. The algorithm consists of two components. Initially, a distributed algorithm for forming a topology is suggested. This algorithm employs a load-based approach to explore network conditions and efficiently choose the most suitable topology. An optimization algorithm that relies on a greedy strategy is suggested. In this approach, the chosen nodes send their bits to the receiver to maximize the attainable system throughput. A thorough simulation study is conducted to evaluate the overall performance of the proposed algorithm in assessing existing methods. The proposed model obtained 94.72 % energy efficiency, 91.69 % network throughput, 94.72 % spectrum utilization, 27.47 % network delay, 24.08 % packet loss rate, 94.38 % signal-to-noise ratio, 93.91 % data transfer rate, 95.87 % error detection rate, and 94.28 % link reliability rate. The results demonstrate that the suggested algorithm significantly enhances the system and the overall network performance compared to existing approaches. The proposed approach is feasible and environmentally friendly for optimizing bandwidth in 5 G wireless ad hoc Networks. 2024 The Authors -
Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process. The Author(s) 2024. -
Improving crop production using an agro-deep learning framework in precision agriculture
Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. The Author(s) 2024.