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Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data’s complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RL-block uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a real-life numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively.

Retinitis pigmentosa (RP) is a group of genetic retinal disorders characterized by progressive vision loss, culminating in blindness. Identifying pigment signs (PS) linked with RP is crucial for monitoring and possibly slowing the disease’s degenerative course. However, the segmentation and detection of PS are challenging due to the difficulty of distinguishing between PS and blood vessels and the variability in size, shape, and color of PS. Recently, advances in deep learning techniques have shown impressive results in medical image analysis, especially in ophthalmology. This study presents an approach for classifying pigment marks in color fundus images of RP using a modified squeeze-and-excitation ResNet (SE-ResNet) architecture. This variant synergizes the efficiency of residual skip connections with the robust attention mechanism of the SE block to amplify feature representation. The SE-ResNet model was fine-tuned to determine the optimal layer configuration that balances performance metrics and computational costs. We trained the proposed model on the RIPS dataset, which comprises images from patients diagnosed at various RP stages. Experimental results confirm the efficacy of the proposed model in classifying different types of pigment signs associated with RP. The model yielded performance metrics, such as accuracy, sensitivity, specificity, and f-measure of 99.16%, 97.70%, 96.93%, 90.47%, 99.37%, 97.80%, 97.44%, and 90.60% on the testing set, based on GT1 & GT2 respectively. Given its performance, this model is an excellent candidate for integration into computer-aided diagnostic systems for RP, aiming to enhance patient care and vision-related healthcare services.

Optical Mark Recognition (OMR) systems have been studied since the 1970s. Due to its simplicity of use and efficiency in bulk operations, OMR technology has been gaining popularity over time. They are used as an automated data input technique for surveys and multiple-choice question papers in educational institutions for automatic evaluation and grading of student inputs. The requirement of the conventional OMR systems comprises specialized OMR machines or optical scanners with automatic document feeding capability. These machines and scanners are fixed-location devices and cannot be moved easily. Their energy requirements are high, while they also require human efforts to operate. These machines are expensive and, hence pose budget constraints for small educational institutions. Due to being mechanical, their maintenance and operating cost is high. To overcome these limitations, alternate devices are smartphone cameras, which though handy adversely lack the capability of scanning documents in a controlled environment. An uncontrolled environment leads to inputs that existing OMR algorithms do not recognize at large, while the accuracy rate and precision stay low to an undesirable extent. Due to this shortcoming, the usage of smartphone cameras is still not feasible. In this experimental study, we have proposed an OMR algorithm specifically for inputs taken from smartphones equipped with decent cameras and running Android or iOS operating systems. Thus effectively, we have ported the OMR technology to smartphones, offering more flexibility, easiness, and mobility of its usage in daily life. The key issue that transpired in our experiments is the bad illumination in different lighting conditions. Our results are very promising and comparable to those obtained from the usage of optical scanners.

Enhancing the crop yield predictability in intercropping systems is important for optimizing agricultural productivity. However, accurately predicting yield in such systems is quite challenging due to complex interactions between crops. This study introduces an advanced methodology using integrated loss functions within an optimally driven Feedback Neural Network (FNN) approach to improve yield prediction in a pea-cucumber intercropping systems. Traditional models relying only on Mean Square Error (MSE) loss function often unable to capture the complexity of models, leading to suboptimal performance. To address this limitation, the advanced loss functions are introduced like Dynamic Margin Loss (DML), Risk-Adjusted Loss (RAL), Quantile Loss (QL), and Hybrid Agronomic Efficiency Loss (HAEL) along with three optimizers such as Adaptive Momentum (Adam), Root Mean Square Propagation (RMSprop), and Adaptive delta (Adadelta). These loss functions incorporate risk, uncertainty, and agronomic efficiency into the model training process, enhances predictive capabilities and robustness. This proposed framework is able to capture the complexity of yield prediction by incorporating agricultural factors. While Gradient Boost Machines (GBM) and Long Short Term Memory (LSTM) have some potential, they are not able to capture these dynamics. The sensitivity and weight analysis also focuses that HAEL targets important agronomic factors such as nitrogen uptake and residue biomass, which provide a holistic view of yield prediction. The proposed approach improves the predictive performance compared to traditional models and helps to identify the importance of features, which makes it an effective tool for decision making in sustainable agriculture. Selecting appropriate loss functions is essential to improve the accuracy and robustness of crops yield prediction models. Thus, study provides a strong foundation for enhancing yield prediction in intricate intercropping systems, which all significantly enhance the advancement of precision agriculture.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

Regression testing is carried out to test the updated supply code within the constraints of time and sources. Since it is very difficult to run all the updated source code every time, test case prioritization is needed to decrease the fee of regression testing. Various methodologies including extensions of white box and black box prioritization, have been presented considering the prioritization of test instances. In this context, the employment of particle swarm optimization (PSO) is usually recommended for test case prioritization. Single test case prioritization focuses to order test cases to maximize objectives like fault detection rate, execution time, etc. Regression testing for single-objective test suite prioritization can become challenging due to its longer execution time. However, test case prioritization for multi-objective functions is a complex and time-consuming task. A check suite may be organized in a certain order by an appropriate technique, subsequently permitting the detection of flaws as early as possible. Multi-goal particle swarm optimization (MOPSO) is used for case prioritization in regression testing. The purpose of MOPSO in this context is to organize the test suite in a specific order that maximizes fault coverage, provides sufficient coverage of test cases, and minimizes execution time. This study proposes an approach based on MOPSO that focuses on maximum fault coverage, most circumstance insurance, and minimal execution time. Experiments are performed using the average percentage of faults detected (APFD) to evaluate its performance. Performance analysis using APFD consisting of no order, opposite order, and random order indicates that the MOPSO surpasses all the previous techniques and obtains an 85% fault coverage. Moreover, MOPSO is better in terms of execution time, fault detection fee, and early detection capabilities.

Abstract In agriculture, the search for higher net profit is the main challenge in the economy of the producers and nano biochar attracts increasing interest in recent years due to its unique environmental behavior and increasing the productivity of plants by inducing resistance against phytopathogens. The effect of rice straw biochar and fly ash nanoparticles (RSBNPs and FNPs, respectively) in combination with compost soil on bacterial leaf spot of pepper caused by Xanthomonas campestris pv. vesicatoria was investigated both in vitro and in vivo. The application of nanoparticles as soil amendment significantly improved the chili pepper plant growth. However, RSBNPs were more effective in enhancing the above and belowground plant biomass production. Moreover, both RSBNPs and FNPs, significantly reduced (30.5 and 22.5%, respectively), while RSBNPs had shown in vitro growth inhibition of X. campestris pv. vesicatoria by more than 50%. The X-ray diffractometry of RSBNPs and FNPs highlighted the unique composition of nano forms which possibly contributed in enhancing the plant defence against invading X. campestris pv. vesicatoria. Based on our findings, it is suggested that biochar and fly ash nanoparticles can be used for reclaiming the problem soil and enhance crop productivity depending upon the nature of the soil and the pathosystem under investigation.

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

Objective: To assess the role of pregabalin as premedication for preoperative sedation & anxiolysis with two different doses in laparoscopic cholecystectomy patients. Study Design: Prospective, randomized control study Place and Duration of Study: Department of Pharmacology and Therapeutics, Army Medical College, Rawalpindi in collaboration with Department of Anesthesiology, CMH, Rawalpindi from February 2019 to July 2019. Patients & Methods: 96 patients of either gender, aged between 18 to 60 years with ASA grade I-II, undergoing elective laparoscopic cholecystectomy were enrolled in this study. They were randomly divided into three groups having 32 patients each. Group 1 received oral placebo drug, Group 2 received oral pregabalin 150 mg, whereas Group 3 received oral pregabalin 300mg with sip of water 90 mins before the induction of general anesthesia. The effects of drugs on the patient’s level of sedation and anxiety were evaluated at baseline and before the induction of anesthesia using Ramsay Sedation Score and Beck Anxiety Inventory respectively. Results: Premedication with pregabalin 150mg and 300mg significantly produced sedation as compared to placebo. Though, pregabalin 300mg exhibited more sedation than pregabalin 150mg but the difference between them was statistically insignificant (p >0.05). Preoperative administration of pregabalin was related with anxiolysis, with the most prominent results shown by pregabalin 300mg (p 0.05). Conclusions: Premedication with pregabalin 300mg, 90 mins before the induction of general anesthesia is an effective regimen to alleviate preoperative anxiety and sedation.

Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are $\beta $ -Thalassemia carriers then there are 25% chances that each child will have $\beta $ -Thalassemia intermediate or $\beta $ -Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to control $\beta $ -Thalassemia. Generally, identification of the Thalassemia carriers is performed by some quantifiable blood traits determined effectively by high-performance-liquid-chromatography (HPLC) test, which is costly, time-consuming, and requires specialized equipment. However, cost-effective and rapid screening techniques need to be devised for this problem. This study aims to detect $\beta $ -Thalassemia carriers by evaluating red blood cell indices from the complete-blood-count test. The present study included Punjab Thalassemia Prevention Project Lab Reports dataset. The proposed SGR-VC is an ensemble of three machine learning algorithms: Support Vector Machine, Gradient Boosting Machine, and Random Forest. Comparative analysis proved that the proposed ensemble model using all indices of red blood cells is very effective in $\beta $ -Thalassemia carrier screening with 93% accuracy.

A number of techniques for securing plaintext, images and video frames have been developed in cryptography using jointly DNA computing and Chaos Theory. With the advancement of DNA/quantum computing, the threats of security breaches to information have an increasing possibility. In this paper, we propose a symmetric encryption algorithm for color images by extending the current encryption/decryption techniques. Our encryption algorithm is based on three chaotic systems (PWLCM, Lorenz and 4D Lorenz-type), a Secure Hash Algorithm, a scrambler, a chaotic generator and DNA sequence based Linear Feedback Shift Register. We introduce multilevel security to increase the degree of diffusion and confusion. Through experiments, we present security analysis for key irreproducibility and sensitivity, Gray Level Co-occurrence Matrix based analysis, maximum deviation, irregular deviation, entropy, histogram, variance and correlation, number of pixel change rate, unified average cipher intensity, known/chosen-plaintext attacks, mean absolute error, robustness against noises of various types using PSNR and occlusion attacks. It is demonstrated that mostly our proposed encryption algorithm has enhanced performance as compared to contemporary works in information security, while comparable in other cases.

This paper presents the Inverse Complex Generalized Synchronization (ICGS) of non-identical nonlinear complex systems with unknown parameters. Using the philosophy of adaptive integral sliding mode control, an adaptive controller and laws regarding parametric upgradation are designed to realize ICGS and parameter identification of two non-identical chaotic complex systems with respect to a given complex map vector. To employ the control, the error system is transformed into a unique structure containing a nominal part and some unknown terms, which are computed adaptively. Then, the error system is stabilized by using integral sliding mode control. The stabilizing controller for the error system is constructed, which consists of the fractional-order control plus some compensator control. To avoid the chattering phenomenon, smooth continuous compensator control is incorporated instead of traditional discontinuous control. The compensator controller and the adapted law are derived in such a way that the time derivative of a Lyapunov function becomes strictly negative. This scheme is applied to synchronize a Memristor-Based Hyperchaotic Complex (MBHC) Lu system and a Memristor-Based Chaotic Complex (MBCC) Lorenz system, a chaotic complex Chen system and a memristor-based chaotic complex Lorenz system with entirely unknown parameters. The effectiveness and feasibility of the proposed scheme is validated through computer simulation using MATLAB software package.

Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) have been used in this study to forecast the threatening factors of COVID-19. Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The results produced by the study proves it a promising mechanism to use these methods for the current scenario of the COVID-19 pandemic. The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset.

The success of AES encryption standard created challenges for the cryptographers to construct strong substitution-boxes using different underlying approaches. It is because they are solely responsible to decide the robustness of cryptosystem against linear and differential cryptanalyses. With an aim to fulfill the mentioned requirement of robustness, a novel group theoretic and graphical method is proposed to construct S-box with optimal features. Firstly, a strong S-box is generated with the help of orbits of coset graphs and the action of proposed powerful permutation of symmetric group S256. In addition, a specific group is designed the action of whose pairs of permutations has the ability to generate as many as 462422016 strong S-boxes. Few of such proposed S-boxes are reported and assessed against standard performance parameters to validate the effectiveness of proposed findings. The features of proposed S-boxes are compared with most of the recent S-boxes to validate the superior performance. Moreover, they are also applied for image encryption to demonstrate their suitability for multimedia security applications.

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

This paper presents the control design method for the transmission projective synchronization (TPS) of the Multiple Non-identical Coupled Chaotic systems using sliding mode. A total of four different cases of chaotic systems are studied which are: 1) systems with known parameters without fault; 2) systems with known parameters with a fault; 3) systems with unknown parameters without fault; and 4) systems with unknown parameters and fault occurrence. In first and third cases, the controllers are designed using sliding mode, and adaptive integral sliding mode (AISM) is used to design controllers for the second and fourth cases. To employ AISM, the error dynamics is broken into a structure comprising a nominal and some unknown part, which are adaptively computed. The error dynamics are stabilized by AISM control which consists of a nominal and a compensator control. To avoid the chattering phenomenon, smooth continuous compensator control is used instead of the traditional discontinuous control. The stability of adaptive law and compensator is derived using a Lyapunov function which becomes strictly negative. Finally, the simulations of a numerical example verify the TPS behavior.

Distributed agile software development is a promising paradigm, addressing the necessities of emergent software application markets that are described by huge user base and small time to market characteristics. A key decision involved during the development process, is task allocation to team members. An appropriate task-member assignment facilitates project management, lessens the complexities and influences chances of project success. Task allocation becomes a more challenging activity in a distributed agile software development due to insufficient understanding of different factors and dependencies involved. We propose a task allocation framework comprising of two phases: one, identifying factors and dependencies that strongly influence the task allocation decision; two, proposing a quantitative method that allocates tasks to team members who best match the task requirements. Task requirements are expressed as capabilities, catering for different aspects, such as technical, personal, and environment. Our method stays transparent to the targeted objectives, in this case the best match. Other objectives, such as quality and cost may be introduced conveniently; even multiple objectives can be addressed. Such a method also allows quality evaluation of task-member assignment during and after the project completion, toward minimizing related risks.

Internet has revolutionized the human life. SEs (Search Engines) are one of the major tools being used for finding information over the Internet. SEs enlist the information into links as per relevance to the searched query. A searcher usually visits the top web links retrieved on SERPs (Search Engine Results Pages) in response to a search query. With the evolving nature of Internet and the increasing number of competitors; it is hard to maintain high ranking in SERPs even for professional correspondents. However, correspondents can apply the techniques of web micro-data to achieve high CTR (Click through Rate) in SERPs. Ranking in major SEs is still a critical factor, although in certain cases such as movies, books, recipes rich snippets proved profitable for webmasters. This study aims to address the gap in micro-data moving from top category such as Animals to their limited scope. Animals with information such as name, price, category will have high CTR and hence more user satisfaction for specified result will lead to high ranking in SERPs.

This study presents the design and implementation of a home automation system that focuses on the use of ordinary electrical appliances for remote control using Raspberry Pi and relay circuits and does not use expensive IP-based devices. Common Lights, Heating, Ventilation, and Air Conditioning (HVAC), fans, and other electronic devices are among the appliances that can be used in this system. A smartphone app is designed that helps the user to design the smart home to his actual home via easy and interactive drag & drop option. The system provides control over the appliances via both the local network and remote access. Data logging over the Microsoft Azure cloud database ensures system recovery in case of gateway failure and data record for lateral use. Periodical notifications also help the user to optimize the usage of home appliances. Moreover, the user can set his preferences and the appliances are auto turned off and on to meet user-specific requirements. Raspberry Pi acting as the server maintains the database of each appliance. HTTP web interface and apache server are used for communication between the android app and raspberry pi. With a 5v relay circuit and micro-processor Raspberry Pi, the proposed system is low-cost, energy-efficient, easy to operate, and affordable for low-income houses.

Instrument fracture during procedure is not uncommon for dental surgeons, especially in root canal surgeries, usually inside the root canals. In rare instances, high-speed rotary instruments can be fractured and can be dislodged in key anatomical areas of face. In our case report, a high-speed dental burr most probably penetrated the root and was seen in the left maxillary sinus during a likely routine dental procedure. The work-up and endoscopic surgical management of the case is described. Practitioners should be in great care during dental procedures and endodontic treatment to avoid unexpected complications by introducing foreign bodies into maxillary sinus. Any patient presenting with recurrent unilateral facial pain or unilateral sinus symptoms with/without previous history of sinusitis should raise the suspect of a foreign body in the paranasal sinus regardless of any previous history of dental procedure.

In the above article [1], the funding information should read “This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2019R1A2C1006159, and in part by the Ministry of Science and ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) support program supervised by the Institute for Information Communications Technology Promotion (IITP), under Grant IITP-2020-2016-0-00313.”