bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural investigation of Amphibalanus amphitrite cement proteins: an in silico study
Authors: Assadizadeh Mohammad , Goodarz Nima , Pak Amir Hossein Mahdavi , Haghayeghi Seyyed Mohammad Hasan , Irani Maryam Azimzadeh
Published: October 2023

Balanomorpha, commonly known as barnacles, are leading biofouling animals belonging to subclass Cirripedia that adhere durably to different submerged surfaces by utilizing a chiefly proteinaceous cement. According to prior experiments, adhesion is most likely made possible by the self-assembling aggregates reputed as amyloid-like nanofibers. The secreted cement contains numerous proteins, among which CP19k and CP20k are thought to have a substantial influence on the adhesion process. The molecular configuration and atomistic interactions that result in this firm cement are not yet completely understood. Herein, artificial-intelligence-based structure prediction and molecular dockings were used to inspect the potential role of the AaCP19k and AaCP20k-1 of Amphibalanus amphitrite in the formation of amyloid-like nanofibers. The anticipated structure of AaCP19k was highly accurate, and its β-sandwich folding had a close resemblance to cross-β motifs found in amyloid nanofibers. In AaCP19k, β1–2 and β7–8 act as oligomerization sites where stable dimers and trimers can be assembled. These modeled oligomerization interfaces point to the self-assembly site through which fibrillization might happen. The structural flexibility of AaCP20k-1 yielded low-accuracy models, but a conserved β-hairpin and an α-helix were evident with high confidence. These structural properties can be employed in prospective studies to develop bioadhesives and design antifouling substances.
Optimisation of adult Anopheles funestus blood-feeding on an artificial membrane feeding system
Authors: Aswat Ayesha, S , Christian Riann , Koekemoer Lizette
Published: October 2023

Malaria is one of the most severe vector-borne diseases caused by Plasmodium parasites and transmitted by Anopheles mosquitoes. Laboratory-reared anophelines are essential to advance research needed to reduce or eliminate malaria. The success of laboratory rearing as well as studies on parasite-mosquito transmission, is advanced by using an artificial membrane feeding systems. These require the optimisation of mosquito feeding to ensure that an optimal number of mosquitoes feed, thereby enabling successful reproduction or research sample sizes. In this study, various parameters such as the type of artificial membrane, density of adults in the feeding cup, age of the mosquito, duration of starvation, method of starvation, the volume of blood meal, duration of feeding, feeding in the light or dark and the effect of lactic acid were evaluated to determine their impact on the feeding rate of a main African malaria vector, Anopheles funestus. By optimising the artificial membrane feeding parameters, an increase in the feeding rate of the An. funestus mosquitoes was observed. The results obtained from these parameters increased the feeding rate of An. funestus above 50%. However, feeding rates were not significantly increased by the type of membrane, mosquito density, the volume of blood meal, duration of feeding and the addition of lactic acid to the cattle intestine membrane. Therefore, this study provides information on suitable conditions for adult mosquito feeding that allows for successful laboratory rearing and colony maintenance. Furthermore, it provides additional information for research studies that are dependent on blood-feeding, such as transmission blocking studies, endectocide studies etc.
Effects of leachate concentration, carbon dioxide and aeration flow rate on chlorophyll and carotenoid productivity and bioremediation potential of the microalga Chlorella minutissima
Authors: Wallyson Ribeiro dos Santos  , Priscila Pereira  , Lucrécio Fábio dos Santos  , Geronimo Virginio Tagliaferro  , Daniela HP Guimarães
Published: October 2023

The use of microalgae cultures to process effluents from industries, leachates, and tanneries, among others, quantified by the reduction of metallic materials in the medium and the reduction of chemical oxygen demand (COD), helps reduce the environmental impact caused by human development. In addition, with the growth of the culture, it is possible to produce a significant amount of chlorophyll, a carotenoid of high value in the cosmetics and food industries that are used as a natural pigment. In this context, this work presents a study conducted to verify the bioremediation and chlorophyll production potential of the cultivation of the microalgae Chlorella minutíssima, using the Taguchi method. The microalgae Chlorella minutissima has given good results in the bioremediation of leachate, as a mean reduction of 33% in COD was obtained, as well as a 92% reduction in the toxic components. In addition, statistical analysis revealed that the four process factors were significant factors for chlorophyll a, chlorophyll b and carotenoid productivity (p < 0.05). Finally, it was observed that the maximum chlorophyll a (111.9 ± 0.8 mg‧L−1‧d−1), chlorophyll b (66.1 ± 1.7 mg‧L−1‧d−1), and carotenoid (31.9 ± 0.03 mg‧L−1‧d−1) values obtained occurred in Experiment 8, which is closer to the ideal conditions identified by statistical analysis, revealing the effectiveness of the use of the Taguchi method for the design of experiments.
Effect of water stratification and mixing on phytoplankton functional groups: a case study of Xikeng Reservoir, China
Authors: Yunhao Bai  , Tinglin Huang  , Pengcheng Yang
Published: October 2023

A shift in reservoir stratification and mixing significantly affects the water column ecosystem, which in turn leads to changes in phytoplankton abundance and community structure. To explore the effects of stratification and mixing on the phytoplankton community structure of a diversion reservoir, a 1-year survey was divided into a stratification period in 2020, a mixing period in 2020, and a stratification period in 2021, and redundancy analysis (RDA), variance partitioning analysis (VPA) and Pearson correlation analysis were used to analyse the key drivers affecting the phytoplankton functional groups, using Xikeng Reservoir as a case study. During the study period, 8 phyla, 69 genera and 9 major functional groups were observed in this reservoir. The dominant functional groups varied significantly, being X1 in the stratified period in 2020; P and D in the mixing period in 2020; and D, X1, and M in the stratified period in 2021. The phytoplankton diversity index was greater in the mixing period than in the stratification period, in agreement with the results of the aquatic ecological status evaluation (Q index, higher in the mixing period than in the stratification period). However, phytoplankton diversity of Xikeng Reservoir was of limited value in assessing the degree of water pollution, so should be considered in combination with the Q index. Water temperature (WT), mixing depth (Zmix), nitrogen–phosphorus ratio (N/P), and total nitrogen (TN) were important drivers of phytoplankton functional group dynamics in different periods. The study provides a valuable reference for assessing the relationship between environmental factors and phytoplankton communities, as well as for the evaluation and conservation of aquatic ecosystems in southern China's water diversion reservoirs.
Silencing METTL14 alleviates liver injury in non-alcoholic fatty liver disease by regulating mitochondrial homeostasis
Authors: Wang Wei , Yan Jun , Han Long , Zou Zi-Lin , Xu Ai-Lei
Published: October 2023

Mitochondrial dysfunction is an important pathogenic factor in non-alcoholic fatty liver disease (NAFLD). Methyltransferase-like 14 (METTL14) has been implicated in mitochondrial fission processes. This research aimed to investigate the mechanism of METTL14 in the mitochondrial function of NAFLD. We first established NAFLD mouse models and cell models, recording body and liver weights and examining pathological changes in liver tissues. Subsequently, serum levels of liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], total cholesterol [TC], and triglycerides [TG]), inflammatory markers (tumor necrosis factor-alpha [TNF-α], interleukin [IL]-6, and IL-1β), and mitochondrial dysfunction indicators (fission 1 protein [Fis1], dynamin-related protein 1 [Drp1], mitofusin 2 [Mfn2], SID1 transmembrane family member 2 [SIDT2], and mitochondrial membrane potential [MMP]) in the liver and cells were evaluated. The N6-methyladenosine (m6A) modification level of primary microRNA (pri-miRNA) and m6A enrichment on pri-miR-34a were quantified. Co-immunoprecipitation and dual-luciferase reporter gene assays were utilized to validate gene interactions. Our findings revealed highly elevated METTL14 expression in NAFLD mouse and cell models. Silencing METTL14 reduced weight gain and mitigated adverse liver function indices, inflammation, hepatic steatosis, and structural damage in NAFLD mice. It also led to a decrease in Fis1/Drp1 levels and an increase in MMP/Mfn2 in the liver and cells. Moreover, METTL14 increased the m6A level, promoting the binding of DiGeorge syndrome critical region 8 (DGCR8) to pri-miR-34a, which enhanced miR-34a-5p expression. Databases and dual-luciferase reporter gene assays indicated that miR-34a-5p could suppress SIDT2 expression. The overexpression of miR-34a-5p or inhibition of SIDT2 expression negated the alleviative effects of METTL14 silencing on mitochondrial homeostasis imbalance. In conclusion, METTL14, through m6A modification, modulates the miR-34a-5p/SIDT2 axis, impairing mitochondrial homeostasis in NAFLD.
Self-healing concrete techniques and performance, A review
Authors:   epartment of Construction and Utilities, Zagazig University, Zagazig, Egypt, Ghazy Ahmed H. , Emara Mohamed R. ,   Department of Structural Engineering, Zagazig University, Zagazig, Egypt, Abdellah Ahmed M. ,   epartment of Construction and Utilities, Zagazig University, Zagazig, Egypt, Attia Mohamed I.E. ,   epartment of Construction and Utilities, Zagazig University, Zagazig, Egypt
Published: October 2023

Modern society faces the duality of rapidly expanding structure, making concrete one of the world's most traded materials. However, cement manufacturing can pollute the environment by releasing approximately one tonne of CO2 for every tonne of cement produced. Concrete cracks can provide superior access for aggressive substances such as chlorides and sulfates, resulting in structural deterioration. So, to fix concrete cracks, different traditional methods were used, which use cement and some chemical agents that are hazardous to the environment. Because of the environmental issues and sustainability challenges associated with cement and concrete, it is preferable to reduce the amount of cement used by developing promising and unique solutions to enable quick crack healing in concrete and extend the structure's lifetime. Therefore, incorporating self-healing mechanisms into construction materials has been proposed to improve their performance and durability while reducing the need for maintenance and repair. This review assesses the performance and causes of autogenous and autonomous self-healing techniques. The autogenous technique occurs naturally due to inherent material properties, while the autonomous technique uses various healing agents, such as chemical or biological substances. Both techniques rely on forming calcium carbonate (CaCO3) crystals as the principal agent for concrete healing. Previous findings showed that the autogenous technique has limited efficacy in repairing larger cracks with a width exceeding 0.3mm. In contrast, autonomous techniques have shown successful repai of cracks exceeding 2mm in width. The application of an autonomous methodology in the field of concrete has resulted in significant results, such as effectively repairing large cracks, enhancing structural integrity, and substantially decreasing permeability levels from high to exceedingly low levels
Recycling of waste lubricating oil using ultra filtration membrane and modeling and prediction of its rheological behavior using Gauss-Newton algorithm
Authors:   Mechanical Engineering Department, National Institute of Technology, Srinagar, India, Nissar Afreen , Hanief M. ,   Mechanical Engineering Department, National Institute of Technology, Srinagar, India, Mir Fasil Qayoom ,   Chemical Engineering Department, National Institute of Technology, Srinagar, India
Published: October 2023

In this research, ultrafiltration membrane made of Polyacrylonitrile (PAN) was employed to recover the lubricating oil from the waste oil in tandem with other treatment methods. FESEM was also used to analyze the structure of the membrane. The resultant recycled oil was monitored with Rheometer, FTIR and UV Spectrometer and it was found that the method successfully removed ˃90% of contaminants and heavy metals from the waste oil and improved the properties of the oil. The proposed method proved to be very economical and gave very promising results to reclaim the waste engine oil that can be re-used for various purposes. Also, the rheological behavior of engine oil is studied by a proposed mathematical model. Given that the suggested model was non-linear, the Gauss-Newton (GN) algorithm was employed to determine the ideal values for the model parameters. The experimental results obtained for virgin, waste and recycled lubricating oils from rheometer were used for validation of the model. The proposed model was compared with Reynold’s exponential model based on statistical methods (co-efficient of determination (R2) and mean square error (MSE)). It was found that that the proposed model can predict the change in viscosity with temperature with very high degree of accuracy (>99%).
Hybrid CNN+LSTM Deep Learning Model for Intrusions Detection Over IoT Environment
Authors: Ghorsad Thamraj Narendra , Zade Amol V.
Published: October 2023

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning-based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a hybrid CNN+LSTM deep learning model for the detection of network intrusions. In this research, we detect DDOS types of network intrusions, i.e., R2L, R2R, Prob, and which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 99.82% as demonstrated in the experimental results.
Exploring Current Trends and Challenges in Cybersecurity: A Comprehensive Survey
Authors: Yamini B. , Radhakrishnan P. , Nalini M. , Maheswari B. , Shanmuganathan M. , R. Siva Subramanian
Published: October 2023

Cyber security is the process of preventing unauthorized access, theft, damage, and interruption to computers, servers, networks, and data. It entails putting policies into place to guarantee the availability, confidentiality, and integrity of information and information systems. Cyber security seeks to protect against a variety of dangers, including as hacking, data breaches, malware infections, and other nefarious actions.  Cyber security has grown to be a major worry as a result of the quick development of digital technology and the growing interconnection of our contemporary society. In order to gain insight into the constantly changing world of digital threats and the countermeasures put in place to address them, this survey seeks to study current trends and issues in the area of cyber security. The study includes responses from end users, business executives, IT administrators, and experts across a wide variety of businesses and sectors. The survey gives insight on important problems such the sorts of cyber threats encountered, the efficacy of current security solutions, future technology influencing cyber security, and the human elements leading to vulnerabilities via a thorough analysis of the replies. The most important conclusions include an evaluation of the most common cyber dangers, such as malware, phishing scams, ransom ware, and data breaches, as well as an investigation of the methods and tools used to counter these threats. The survey explores the significance of staff education and awareness in bolstering cyber security defenses and pinpoints opportunities for development in this area. The survey also sheds insight on how cutting-edge technologies like cloud computing, artificial intelligence, and the Internet of Things (IoT) are affecting cyber security practices. It analyses the advantages and disadvantages of using these technologies while taking into account issues like data privacy, infrastructure security, and the need for specialized skills. The survey also looks at the compliance environment, assessing how industry norms and regulatory frameworks affect cyber security procedures. The survey studies the obstacles organizations encounter in attaining compliance and assesses the degree of knowledge and commitment to these requirements. The results of this cyber security survey help to better understand the current status of cyber security and provide organizations and individual’s useful information for creating effective policies to protect digital assets. This study seeks to promote a proactive approach to cyber security, allowing stakeholders to stay ahead of threats and build a safe digital environment by identifying relevant trends and concerns.
Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems
Authors: Danish Mohd , Amjad Mohammad
Published: October 2023

This research paper proposes a rating-based recommender system that leverages Natural Language Processing (NLP) techniques to enhance the accuracy and effectiveness of recommendations. Traditional recommender systems primarily rely on numerical ratings provided by users to make predictions. However, these ratings often lack detailed information about user preferences and suffer from sparsity and inconsistency issues. By incorporating NLP, we aim to extract valuable insights from textual reviews and improve the recommendation process. Our system utilizes sentiment analysis, topic modelling, and text embeddings to capture the implicit information in reviews and generate more personalized and context-aware recommendations. The experimental results demonstrate the superior performance of the proposed rating-based recommender system compared to conventional approaches.
Development of Image Based Model for Basic Standing Yoga Poses that Control Type-2 Diabetes
Authors: Nandan T. P. Kausalya , Madhavi D.
Published: October 2023

Yoga is one of the ancient practices originated in India that helps in balancing mind and body of human. For the past few decades it has got wide spread throughout the world. Many are practicing it in the presence of yoga tutor or following some online modes. But improper practice may cause major harm to muscles and ligaments of the human body. There are different asanas proposed in the Patanjali Yoga Sutra that can cure different diseases. This paper, proposes a mathematical model for a set of yoga asanas that can help cure Type -2 Diabetes. A noninvasive analysis has been implemented using Kinect Sensor and LabVIEW software to analyze the performance of the practitioner. The joints are subjected to the flexibility of the practitioner without any overstress.
A Novel Skin Disease Detection Technique Using Machine Learning
Authors: H. Rekha , Balamurugan K. S. , Bhuria Vijay , Murthy G. N. Keshava , Vinita Vinita , Rani Bindu
Published: October 2023

Skin sicknesses present critical medical care difficulties around the world, requiring precise and opportune location for successful therapy. AI became promising stuff for computerizing the discovery and characterization of skin illnesses. This study presents a clever methodology that uses the choice tree strategy for skin sickness location. In computerized location, we utilize an exhaustive dataset containing different skin sickness pictures, including melanoma, psoriasis, dermatitis, and contagious diseases. Dermatologists skillfully mark the dataset, guaranteeing solid ground truth for precise grouping. Preprocessing strategies like resizing, standardization, and quality improvement are applied to set up the symbolism for the choice tree calculation. Then, we remove applicable elements from the preprocessed pictures, enveloping surface, variety, and shape descriptors to catch infection explicit examples successfully. The choice tree model is prepared utilizing these removed elements and the named dataset. Utilizing the choice tree's capacity to learn progressive designs and choice principles, our methodology accomplishes an elevated degree of exactness in grouping skin sicknesses. Extensive experiments and evaluations on a dedicated validation set demonstrate the effectiveness of our decision tree-based method, achieving a classification accuracy of 96%. Our proposed method provides a reliable and automated solution for skin disease detection, with potential applications in clinical settings. By enabling early and accurate diagnoses, our approach has the capacity to improve patient outcomes, trim down healthcare overheads, and alleviate the burden on dermatologists.
Application of IoT Framework for Prediction of Heart Disease using Machine Learning
Authors: Swain Satyaprakash , Behera Naliniprava , Swain Anil Kumar , Jayasingh Suvendra Kumar , Patra Kumar Janardan , Pattanayak Binod Kumar , Mohanty Mihir Narayan , Naik Kodanda Dhar , Rout Sefali Surabhi
Published: October 2023

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having  HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.
Fusing Long Short-Term Memory and Autoencoder Models for Robust Anomaly Detection in Indoor Air Quality Time-Series Data
Authors: Brahmam M. Veera , Gopikrishnan S.
Published: October 2023

People spend most of their time indoors by choice or by need. Carbon dioxide (CO2) accumulation can cause various adverse health effects, including vertigo, headache, and fatigue. Therefore, monitoring indoor air quality(IAQ) is necessary for various health reasons. The market is flooded with air quality monitoring devices. However, the ordinary public does not make use of them because they are expensive and difficult to obtain. Several research studies have been carried out to monitor indoor air quality with the help of the Internet of Things(IoT), which has greatly simplified the method for monitoring IAQ. In this research, we offer an improved IoT based IAQ monitoring system with AI-powered recommendations. Our suggested system relies on the Message Queuing Telemetry Transport(MQTT) protocol for communication between IoT devices. In addition, the gathered CO2 occupancy data is used together with the deep learning approach of Long Short-Term Memory and Autoencoder (LSTM-AE) to detect anomalies or outliers in CO2 concentrations.  Due to a close connection between air quality and human health and well-being, the detection of anomalies in the data of  IAQ has emerged as an essential topic of study. Anomalies requiring the observation of correlations spanning numerous data points (i.e., often referred to as long-term dependencies) were not detectable by conventional statistical and basic machine learning (ML) related techniques in the sector of  IAQ.  Hence this research uses the LSTM-AE model to address this issue.  In comparison to previous similar models, our experimental results on a generated CO2 occupancy time series reveal a robust and powerful accuracy of 99.49%.
Optimizing Hyperparameters for Enhanced LSTM-Based Prediction System Performance
Authors: Paygude Priyanka , Chavan Prashant , Gayakwad Milind , Gupta Khushi , Joshi Satyam , Gopika Gopika , Joshi Rahul , Gonge Sudhanshu , Kotecha Ketan
Published: October 2023

This research paper explores the application of deep learning and supervised machine learning algorithms, specifically Long Short-Term Memory (LSTM), for stock market prediction. The study focuses on the closing prices of three companies - Tata Steel, Apple, and Powergrid - using a dataset sourced from Yahoo Finance. Performance evaluation of the LSTM model employed RMSE, MAPE, and accuracy metrics, along with hyperparameter calibration to determine the optimal model parameters. The findings indicate that a single-layer LSTM model outperformed a multilayer LSTM model across all companies and evaluation metrics. Furthermore, a comparison with existing research demonstrated the superiority of the proposed model. The study emphasizes the effectiveness of LSTM models for stock price prediction, underscores the significance of proper hyperparameter tuning for optimal performance, and concludes that a single-layer LSTM model can yield superior results compared to a multilayer model.
AgriBlock: A Blockchain based Framework for Agricultural Supply Chain Management
Authors: Acharya Deepak Kumar , Shrivastava Manish , Padhi Payodhar
Published: October 2023

To facilitate complex interactions between regional players and the agricultural environment, modern agriculture supply chains have arisen. Supply chain processes that include several parties, such as production, processing, and delivery, interacting with one another. Lack of transparency in the agricultural supply chain is a major contributor to the incidence of fraud in this industry. A lack of openness on the part of organisations leads to worries about potential financial losses, diminishing customer trust, and dwindling brand value. Several fundamental changes to the existing supply chain architecture are required to foster the development of an effective and reliable trade environment. It is widely recognised that blockchain technology has the potential to increase visibility across agricultural and food supply chains. Businesses are using blockchain and IoT to implement more egalitarian, sustainable, and consumer-friendly methods of food production. Based on what we learned, blockchain technology may be useful in the push for a more transparent and responsible food supply chain. The primary goal of this study is to create a decentralised application framework called AgriBlock that can be used for managing the supply chains of agricultural products using blockchain technology. The developed work AgriBlock will aid in making the supply chain process more open and clear to every participant involved.
SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network
Authors: Kumar Vemula Kesava , Varma P. Suresh
Published: October 2023

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.
A Comparative Analysis for Filter-Based Feature Selection Techniques with Tree-based Classification
Authors: Parmar Mahesh , Sonker Abhilash , Sejwar Vikas
Published: October 2023

The selection of features is crucial as an essential pre-processing method, used in the area of research as Data Mining, Text mining, and Image Processing. Raw datasets for machine learning, comprise a combination of multidimensional attributes which have a huge amount of size. They are used for making predictions. If these datasets are used for classification, due to the majority of the presence of features that are inconsistent and redundant, it occupies more resources according to time and produces incorrect results and effects on the classification. With the intention of improving the efficiency and performance of the classification, these features have to be eliminated. A variety of feature subset selection methods had been presented to find and eliminate as many redundant and useless features as feasible. A comparative analysis for filter-based feature selection techniques with tree-based classification is done in this research work. Several feature selection techniques and classifiers are applied to different datasets using the Weka Tool. In this comparative analysis, we evaluated the performance of six different feature selection techniques and their effects on decision tree classifiers using 10-fold cross-validation on three datasets. After the analysis of the result, It has been found that the feature selection method ChiSquaredAttributeEval + Ranker search with Random Forest classifier beats other methods for effective and efficient evaluation and it is applicable to numerous real datasets in several application domains
Optimizing Pre-Trained Models of Deep Learning for Identification of Plant Disease
Authors: Prakadeswaran S. , Banu A. Bazilla , Kumar R. Senthil , Raj S. Gokul
Published: October 2023

The Plant diseases should be identified early to prevent the economic loss of farmers and ensure the availability of food for humans. The plant disease identification can be automated by using the Artificial Intelligence techniques. Researchers have proposed many deep learning methods for identifying plant diseases. Deep learning models use an increased number of parameters, it requires higher computational power, training a deep learning model from start requires more time. In this article we utilized transfer learning along with fine tuning for identification of plant diseases. Cassava plant disease dataset was utilised for training. and evaluate the suggested model. The performance accuracy achieved by Resnet50 is 73.12 % and fine-tuned Resnet50 is 80.84 %. The fine-tuned model achieves greater accuracy with a lesser amount of parameters

Impact Statement–Artificial Intelligence is evolving all around the world. The AI techniques are used to automate the process of plant disease identification. Traditional methods are not accurate and time consuming. To help the farmers in diagnosing plant disease and stop economic loss to them, we employ deep learning models to do the work. The pretrained models predict the plant diseases, further we fine-tune them in order to get high accuracy. Early identification of the diseases accurately will avoid loss and improve productivity of the crops.
Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET
Authors: R. Veeramani , Mohan R. Madhan , Mahesh C.
Published: October 2023

Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.
A Novel Hybrid Security Framework (HSF) with Vshield Based Firewall to Secure Cloud Computing Environment
Authors: Praise J. Jeya , Muthukumaran N. , Raj R. Joshua Samuel
Published: October 2023

Cloud Computing is an emerging technology that provides an enormous amount of computing resources which includes networks, servers and storages which are accessed through the internet. In addition it allows useful provisioning of the resources based on the user’s demands. A crucial aspect of cloud computing infrastructure is to provide secure and reliable services.  The main challenge lies in the security issues is to reduce the impact of third party attacks in the cloud computing environment. Hence a novel Hybrid Security Framework(HSF) based on Reinforcement Learning (RL) Methodology with Vshield Firewall is proposed for securing the cloud environment.  The RL method is used for deep packet inspection and VShiled based firewall is established to deny the attacks which are malicious when authenticating the signature of incoming packets. The bipartite pattern matching approach is integrated with the RL method to verify the signatures for obtaining the decisions quickly.  The simulation results shows that the hybrid security framework is effective when compared with the existing methods by considering response time, resource utilization and denial of malicious attacks.  This indicates that our proposed framework achieves not only better security but also attains better efficiency in cloud computing environment.
Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm
Authors: Mule Nilakshi Maruti , Patil Dipti Durgesh
Published: October 2023

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others.
DIB - A Novel Optimized VANET Traffic Management Using a Deep Neural Network
Authors: Sankar P. , Joel M. Robinson , Husain A. Jahir
Published: October 2023

The advancement of the Internet of Things (IoT) establishes the development of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS).  An integral part of ITS is the vehicular ad hoc network (VANET) with smart vehicles (SV).   In this research, a dynamic method of traffic regulation in VANET is presented using Deep Neural Networks (DNN) and Bat Algorithms (BA). With a reduced average delay, the former (DNN) is utilized to guide vehicles across very crowded routes to increase efficiency. In order to examine the traffic congestion status between network nodes, BA is integrated with the IoT and moved over VANETs. Experiments were conducted to test the effectiveness of the proposed method with various parameters such as average latency, packet delivery ratio (PDR) and throughput and the performance were compared with different machine learning (ML) algorithms.  The simulation outputs proved that the proposed technique supports real-time traffic circumstances with less energy usage and delay than existing methods.
Rice Leaf Disease Detection Using Convolutional Neural Network
Authors: P.K Rajani , Deshmukh Vaidehi , Bhandari Sheetal U. , Raut Roshani , Kharat Reena
Published: October 2023

In the agriculture sector, the rice crops getting diseased has become a significant concern recently, especially in India, where rice is one of the primary meals. Precise and early-stage detection of various diseases observed in the rice crops can help farmers to provide proper treatment to the crops. This paper presents a Convolutional Neural Network (CNN) based approach is used to detect rice plant leaf disease. CNN is one of the deep learning algorithms that help in image processing and classification with significant accuracy. The proposed algorithm is used for an image dataset of the diseased rice plant leaves, available on Kaggle. Two types of rice leaf diseases are considered for the analysis: brown spot and bacterial leaf blight. The images of these two diseases were pre-processed, segmented, and classified to identify the caused disease. The proposed model can also be used for the detection of the diseases present in other types of crops, faces recognition system, classifying animals, and car models. The overall accuracy of the developed model is nearly 67%.
Comparision of Different Classifiers for Prediction of Breast Cancer
Authors: P B Tintu , Priya S Manju
Published: October 2023

The cell formed in the  breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set.  Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques.
FLY-CAPS- A Hybrid Firefly Feature Optimized Capsule Networks for Plant Disease Classification in Resource Constriant Internet of Things (IoT)
Authors: Kalpana Ponugoti , Anandan R.
Published: October 2023

Recent advancements in artificial intelligence, automation, and the Internet of Things (IoT) enable farmers to better monitor and diagnose all agricultural procedures with super-intellectual accuracy. These technologies also contribute to boosting the productivity of agriculture, which increases the country’s economy. Though these technologies help farmers increase productivity, the detection of plant diseases still needs heightened scrutiny for prevention and cultivation. Plant disease categorization has expanded with the introduction of deep learning algorithms, but it still needs more innovation in terms of accuracy and computing burden. Thus, a novel deep learning model based on capsule networks with firefly optimization and potent multi-layered feedforward prediction networks is proposed in this research. The handcrafted features in this proposed system are optimized before being extracted using a capsule network, which reduces the complexity overhead and is suitable for IoT devices with limited resources. Finally fed to the feed forward layers for better classification. The extensive experimentation has been tested with the Plant Village databases, which contain more than 50,000 images of healthy and infected plants. Performance criteria including recall, specificity, recall, accuracy, and f1-score are used to assess the proposed algorithm's performance. Additionally, its efficiency and computational cost are contrasted with those of other recent models. The suggested model has greater performance (95%) with reduced computing overhead, according to experimental data, which is advantageous for the new prediction approach and the welfare of the farmer.
Nurturing Agribusiness: A Sustainable Farming System for Tomato Crop Management Leveraging Machine Learning
Authors: Praksh B. Leelaram , Gundavarapu Mallikarjuna Rao , Latha S. Bhargavi , Raju S Ravi Kumar , Kumar NVS Pavan
Published: October 2023

The agriculture industry is undergoing a transformative shift with the introduction of IoT technology, enabling global connectivity for farmers. This technology offers a plethora of advantages, ranging from precise seed selection based on soil analysis to efficient crop maintenance, water management, and enhanced marketing support for improved profitability. To further enhance tomato farming practices, we propose the implementation of a smart farmer marketing assistant that streamlines the process of segregating yield based on its growth stage, reducing labor and time requirements.Further, the frame work is capable of early-disease management system that can detect  diseases like early blight,light blight, buck eye rot and anthranose and suggest remedy.  By adopting this innovative approach, financial losses associated with traditional methods are minimized.The traditional practice of combining all categories of vegetables (ripened, unripened, and partially rotten) in a single container often results in reduced shelf life for the produce. In our framework, we employ color sorting to categorize the vegetables, ensuring proper packing into their respective bins. This valuable data is made accessible through a cloud environment, providing potential buyers with comprehensive information about the yield, its category, and pricing. This increased visibility empowers farmers to reach a global market and sell their produce at competitive prices.

In this context, we present a case study focused on the tomato crop, where we have successfully developed a prototype utilizing ESP32, a color sensor, and Google Firebase. This comprehensive framework effectively harnesses the power of IoT, Machine Learning, and potential marketing strategies, transforming the way farmers manage their crops and connect with buyers on a global scale with highly accurate 87.9% experimental results.