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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.

The presentation layer is the outermost layer of an application that providesuser interface and communication services. This layer is responsible for session management, controlling client access, and validations within data from the client.In legacy enterprise applications like Java Enterprise Edition Platform (Java EE),thedesign considerations of the presentation layer are spread over different design patterns and cross-language constructs. Resultantly, the analysis of such applications becomes quite challenging due to their heterogeneity, essentially requiredforthe extraction of design-level information and furthermodernization. In this research,a flexible technique is presented to extract presentation tier information based on customizable feature types by recovering instances of presentation tier patterns of the Java Enterprise EditionPlatform.The proposed approach is evaluated on well-operative open-source Enterprise Applications. The validation resultsdemonstratethe extraction of presentation tier information through Design Pattern’s recovery.This prototype is validated on the repository of source code of Java applications as well on open source java applications.

Lower Respiratory Tract Infection (LRTI) is the leading global cause of morbidity and mortality in children of 1 month in developing countries. The aim of this research was to examine the spatial patterns of children under LRTI in Lahore, Pakistan. The records of all patients of LRTIs among children <5 years, admitted in the four different public sector hospitals of Lahore from 2017-2021 were analyzed. The collected data was processed and analyzed in SPSS 22.0 for the chi-square test (P<0.0.5), Multiple linear regression and ANOVA were calculated to assess the association of these variables. Town-wise distribution of diseases was mapped in ArcGIS 10.5. There were 2,609 pediatrics patients admitted and major cases in the year 2021. All the patients were distributed in four age groups, <2m, 2-12m, 13-24m, 25-60m. The most common diagnosis was Bronchopneumonia with (77.50%), Bronchiolitis (11.84%), Pneumonia (6.86%), and Bronchitis (3.79%). A significant increasing trend was found in Bronchopneumonia. In town-wise analysis, out of 2,609 patients, 977 patients were observed in Allama Iqbal Town. The peak season of the disease was seen in winter Dec-Feb. LRTI is a leading cause of childhood hospitalization in Lahore, Pakistan. These results may guide health authorities to determine where and when to effectively allocate resources for the prevention and control of LRTI.

Microorganisms are an important part of the food industry as these are helpful in food preservation and production. Usually, microorganisms are used in making dairy products (yogurt and cheese), fermented vegetables (olives, pickles, and sauerkraut), fermented meats (salami), and sourdough bread. These are also utilized for the production of wine and several other beverages. Recently in the food industry, the use of microorganisms has started on a large scale for the production of chocolate, food color, from preserving fruits, vegetables and meat, and as probiotics which are helpful for human health. Different types of the microorganisms produce enzymes of nutritional value such as microbial transglutaminase for fish production. As the human population is increasing, we need to adopt new techniques for producing qualitative and nutritious food. These microorganisms can be used to cope with the shortage of food supply. This review will brief the role of microorganisms in above mentioned products as a leading step towards the modern food industry.

The study is a comparative analysis of water quality among two variant areas of Lahore. There are several problems regarding drinking water facilities. Drinkable water can be contaminated due to various reasons. Thus, the study highlights infrastructural causes (material of pipes and outdated pipes) of water contamination. Wall City and Gulberg are the study areas of this research. Gulberg area is far much better in various terms as compared to the wall city. Under this study, four parameters were selected for water quality pH, Total dissolved solids, E.coli and Arsenic. There were 13 water samples collected from each study area by random sampling. Samples were tested on the latest footing in this field. All results validate the problematic statement and highlight severe health effects. The results of these four parameters were far above the water quality standards declared by World Health Organization. Causes of these severe results include the outdated water pipes that are being laid down for the past several decades, for example Wall City area, etc. Results also depict low values in the Gulberg area which is recently developed as compared to the wall city. The comparative study also attests problem statement of the study.

Depression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (SVM) had acquired 95.7% accuracy. The developed Deep Learning approach, convolution neural network (CNN), achieved 99.6% accuracy. With these promising results, this study establishes the viability of an Electroencephalogram based diagnosis of MDD.

Tomatoes have enhanced vitamins that are necessary for mental and physical health. We use tomatoes in our daily life. The global agricultural industry is dominated by vegetables. Farmers typically suffer a significant loss when tomato plants are affected by multiple diseases. Diagnosis of tomato diseases at an early stage can help address this deficit. It is difficult to classify the attacking disease due to its range of manifestations. We can use deep learning models to identify diseased plants at an initial stage and take appropriate measures to minimize loss through early detection. For the initial diagnosis and classification of diseased plants, an effective deep learning model has been proposed in this paper. Our deep learning-based pre-trained model has been tuned twofold using a specific dataset. The dataset includes tomato plant images that show diseased and healthy tomato plants. In our classification, we intend to label each plant with the name of the disease or healthy that is afflicting it. With 98.93% accuracy, we were able to achieve astounding results using the transfer learning method on this dataset of tomato plants. Based on our understanding, this model appears to be lighter than other advanced models with such considerable results and which employ ten classes of tomatoes. This deep learning application is usable in reality to detect plant diseases.

The rapid advancement of the internet and its exponentially increasing usage has also exposed it to several vulnerabilities. Consequently, it has become an extremely important that can prevent network security issues. One of the most commonly implemented solutions is Intrusion Detection System (IDS) that can detect unusual attacks and unauthorized access to a secured network. In the past, several machine learning algorithms have been evaluated on the KDD intrusion dataset. However, this paper focuses on the implementation of the four machine learning algorithms: KNN, Random Forest, gradient boosted tree and decision tree. The models are also implemented through the Auto Model feature to determine its convenience. The results show that Gradient Boosted trees have achieved the highest accuracy (99.42%) in comparison to random forest algorithm that achieved the lowest accuracy (93.63%).

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions.

Sepsis is a very fatal disease, causing a lot of causalities all over the world, about 2, 70,000 die of Sepsis annually, thus early detection of Sepsis disease would be a remedy to prevent this disease and it would be a big relief to the family of sepsis patients. Different researchers have worked on sepsis disease detection and its prediction but still the need to have an improved model for Sepsis detection remains. We compared various machine learning algorithms for Sepsis detection and used the dataset publicly available for all the researchers at Physionet.org, the dataset contains many empty or Null values, we applied backward filling and forward filling techniques, and we calculated missing values of MAP using equation (1) which gives more precise results, we divided the 40,336 files of datasets A and B into 80% training set and 20% testing set. We applied the algorithms twice one time using vital signs and clinical values of patients and the second time using only vital signs of the patients; using vital signs only the training accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.992, 0.999, 0.981, 0.981, and 0.981 respectively, while the testing accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.987, 0.980, 0.983, 0.981, and 0.981 respectively, for Sepsis Label 0, the value of precision for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 0.99, 0.98, 0.98, 0.98, and 0.98 respectively, while the value of recall for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 1.00, 1.00, 1.00, 1.00, and 1.00 respectively; the comparison of all the above-mentioned algorithms showed that KNN leads over all the competitors regarding the accuracy, precision, and recall.

Fingerprints are commonly used in biometric systems. However, the authentication of these systems became an open challenge because fingerprints can easily be fabricated. In this paper, a hybrid feature extraction approach named Integration of Probability Weighted Spatial Gradient with Ridge Orientation (IPWSGRo) has been proposed for fingerprint liveness detection. IPWSGRo integrates intensity variation and local ridge orientation information. Intensity variation is computed by using probability-weighted moments (PWM) and second order directional derivative filter. Moreover, the ridge orientation is estimated using rotation invariant Local Phase Quantization (LPQri) by retaining only the significant frequency components. These two feature vectors are quantized into predefined intervals to plot a 2-D histogram. The support vector machine classifier (SVM) is then used to determine the validity of fingerprints as either live or spoof. Results are obtained by applying the proposed technique on three standard databases of LivDet competition 2011, 2013, and 2015. Experimental results indicate that the proposed method is able to reduce the average classification error rates (ACER) to 5.7, 2.1, and 5.17% on LivDet2011, 2013, and 2015, respectively.

Melanoma is a skin lesion disease; it is a skin cancer that is caused by uncontrolled growth in melanocytic tissues. Damaged cells can cause damage to nearby cells and consequently spreads cancer in other parts of the body. The aim of this research is the early detection of Melanoma disease, many researchers have already struggled and achieved success in detecting melanoma with different values for their evaluation parameters, they used different machine learning as well as deep learning approaches, and we applied deep learning approach for Melanoma detection, we used publicly available dataset for experimentation purpose. We applied deep learning algorithms ResNet50 and VGG16 for Melanoma detection; the accuracy, precision, recall, Jaccard index, and dice co-efficient of our proposed model are 92.3%, 93.3%, 90%, 9.98%, and 97.7%, respectively. Our proposed algorithm can be used to increase chances of survival for patients and can save the money which is used for diagnosis and treatment of Melanoma every year.

Land modification and its allied resources have progressively become a severe problem presently pulling the worldwide attention and now it rests at the central point of the conservation of the environment and sustainability. The present research aimed to examine the land-use changes and their impact on agricultural production using remote sensing and GIS techniques over the study area that comprised of Tehsil Shorkot, District Jhang, Punjab, Pakistan. Images were pre-processed by using the Arc GIS and ERDAS Imagine 15 software for stacking of the layers, sub-setting, and mosaicking of the satellite bands. After the pre-processing of the images, supervised image classification scheme was applied by employing a maximum likelihood algorithm to recognize the land-use changes which have been observed in the area under study. The area under water was occupied 9.6 km2 in 2010 that increased to 21.04 km2 in 2015 and decreased to 19.4 km2in 2020. Built-up land was 16.6 km2 in 2010 that increased to 19.4 km2 in 2015 and 26.8 km2 in 2020. The total area under vegetation was computed as 513.2 km2 in 2010 that increased to 601.6km2 in 2015 and further increased to 717.7 km2in 2020. Forest land use showed decreasing trend as the covered area in 2010 was occupied 90.8 km2 that decreased to 86.7 km2 in 2015 and further decreased to 61.84 km2 in 2020. In 2010, barren land use was occupied 528.54 km2 that considerably decreased to 429.64 km2 in 2015 further decreased to 333.1 km2 in 2020. Barren land drastically decreased into watered, built-up, and vegetation land uses. The findings of this study will be helpful for the future conservation of various land-use types, urban and regional planning, and an increase in agricultural production of various crops in the study area.

The soil is the basic medium for growth of plant as it supplies essential nutrients and water required for plant processes. The productivity of crop is highly dependent upon fertility and salinity of soil. Current study was carried out to explore and analyze the soils of Tehsil Nankana Sahib (Nankana, Shahkot, Sangilla) for its salinity, sodicity and fertility status at union council level from 2018-2021. A total 2030 soil samples were collected from three Tehsils of District Nankana Sahib, Punjab, Pakistan. The results indicated that the soil salinity status about 33.9% (690 samples) soils were non-saline, 23.6% (480 samples) saline sodic, 28.5% (580 samples) sodic and only 13.8% (280 samples) were saline. Maximum problematic soil was found in tehsil Nankana Sahib while minimum in Sangilla. As for the soil fertility status of District Nankana Sahib is concerned, 60.1% soils were poor in organic matter (OM) that was observed in 1220 samples, and 39.1% medium range organic matter was observed from the 794 samples while 7.8% from the only 160 samples that were approaching the adequate range. The available phosphorus in soils was found poor among 26.1% (530 samples), 56.1% medium (1140 samples) and the adequate range of available phosphorus was 17.7% (360 samples).

The role of albedo is very important in modulating the surface energy balance of glaciers. The main objective of this study is to assess the spatiotemporal variability in surface albedo of the Karakoram glaciers in Pakistan during the summer seasons (June, July and August) for the period from 2000-2018. We used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the amount of glacier surface albedo. We combined the MODIS Terra- and Aqua-derived albedo products to reduce the amount of cloud influence and to improve the estimation of glacier surface albedo. Our results indicate that the average annual decrease in albedo is ~0.041% during the summer. The decrease in albedo was relatively high during recent years, with an annual rate of decrease of ~0.45%. The decreasing trend in albedo is towards the north-western part of the Karakoram mountain range. Climate change is the potential cause of albedo variations in the study area. Albedo has a strong negative correlation with temperature (r = -0.811) and a strong positive correlation with precipitation (r = 0.809). The present study concludes that trend in decreasing albedo is higher during the recent years than the last decade and climate change is playing a vital role in it.

In recent years, we have witnessed a rapid rise in the mortality rate of people of every age due to cardiac diseases. The diagnosis of heart disease has become a challenging task in present medical research, and it depends upon the history of patients. Rapid advancements in the field of deep learning. Therefore, it is a need to develop an automated system that assists medical experts in their decision-making process. In this work, we proposed a novel optimized grid search-based multi-layer perceptron method to effectively detect heart disease patients earlier and accurately. We evaluated the performance of our method on a dataset named Public Health dataset for heart diseases. More specifically, our method obtained an accuracy of 95.12%, precision of 95.32%, recall of 95.32%, and F1-score of 95.32%. We made a comparison of our method with existing methods to check superiority and robustness of our system to detect heart disease patients. Experimental results along with comprehensive comparison with other methods illustrate that our technique has superior performance and is robust to detect heart disease patients. From the results, we can conclude that our method is reliable to be used in hospitals for the early detection of heart disease patients.

This article suggests the preventive measures for healthcare department (particularly radiotherapy department) to reduce the probability of corona virus transmission with a resource constrained approach without affecting the work flow. COVID-19 has affected the patients as well as staff of radiotherapy department leaving a severe negative impact on the financial resources of INOR cancer hospital, Abbottabad. Multiple preventive measures have been taken to reduce the probability of spreading the coronavirus while pursuing the timely treatment of radiotherapy patients without compromising their oncological outcomes. In this context, a triage center was established to filter out the Covid suspected/confirmed patients to reduce the risk of infection to other patients and staff. Social distancing was ensured by making amendments in patient gathering areas. Also extensive ventilation and disinfection procedures were adopted to clean the surfaces. Following these measures, patient flux did not show any considerable decrease in second, third and fourth wave as compared to first wave when patient flux reduced to about less than 25 %. Preventive measures were also taken for the employees by ensuring them to wear personal protective equipment during office hours. To further reduce the probability of contact, telemedicine was adopted for patients where possible. All employees were made to be fully vaccinated by July 2021 resulting in 100 % reduction in new cases among INOR employees in the following fourth COVID wave. Owing to these stringent measures taken to fight against coronavirus, ratio of contracting the coronavirus among the employees and patients of INOR has been found <10% overall in this pandemic, While no mortality has been reported so far.

The relational database is the way of maintaining, storing, and accessing structured data but in order to access the data in that database the queries need to be translated in the format of SQL queries. Using natural language rather than SQL has introduced the advancement of a new kind of handling strategy called Natural Language Interface to Database frameworks (NLIDB). NLIDB is a stage towards the turn of events of clever data set frameworks (IDBS) to upgrade the clients in performing adaptable questioning in data sets. A model that can deduce relational database queries from natural language. Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. In this paper, we reviewed the existing framework and compared them based on the aggregation classifier, select column pointer, and the clause pointer. Furthermore, we discussed the role of semantic parsing and neural algorithm’s contribution in predicting the aggregation, column pointer, and clause pointer. In particular, people with limited background knowledge are unable to access databases with ease. Using natural language interfaces for relational databases is the solution to make natural language to SQL queries. This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their framework and to highlight the limitations in the existing models.