Volume 12 | Issue 5

 Abstract

In the fight against brain cancer, accurate brain tumor detection is crucial for early diagnosis and effective treatment. This study explores various machine learning and deep learning techniques to achieve this goal using MRI scans. We investigated the effectiveness of several methods: Convolutional Neural Networks (CNNs) achieved an impressive test accuracy of 86.27%. This demonstrates their ability to learn important features directly from MRI images. Multilayer Perceptrons (MLPs) were explored in two ways. A standalone MLP trained on features extracted using Principal Component Analysis (PCA) reached an accuracy of 76.47%. We also experimented with using an MLP in a transfer learning approach with InceptionV3 for feature extraction. This approach yielded results to be discussed alongside the standalone models. We also compared the performance of several other machine learning techniques alongside the MLPs and CNNs. A standalone MLP, trained on its own without any transfer learning, achieved an accuracy of 52.94%. We also evaluated the VGG16 convolutional neural network, which reached an accuracy of 70.58%. Logistic regression, a common statistical method, yielded an accuracy of 62.74%. Random forest, an ensemble learning technique that combines multiple decision trees, achieved an accuracy of 72.54%. Ada boosting, another ensemble learning method, performed quite well, reaching the highest accuracy (74.50%) among all the non-deep learning models. For other machine learning models, Naive Bayes achieved an accuracy of 68.62%, while SVM (Support Vector Machine) reached 60.78%. Similarly, a decision tree model resulted in an accuracy of 68.62%. Bagging, another ensemble technique, yielded an accuracy of 66.66%. Interestingly, a hybrid model that combined the pre-trained VGG-16 and InceptionV3 models achieved an accuracy of 68.92%.The results reveal that Convolutional Neural Networks (CNNs) were the most successful method, achieving the highest accuracy (86.27%) for brain tumor detection in MRI scans. This suggests that CNNs are particularly adept at learning the critical patterns hidden within the MRI image data.

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The rise of non-communicable diseases (NCDs), such as heart disease and diabetes, underscores the crucial necessity for innovative predictive solutions. This study offers a framework for improving the detection, diagnosis, and management of cardiovascular diseases (CVDs) by machine learning algorithms. Recognizing the intricate relationship between modernization, commercialization, and unhealthy lifestyles, our research presents a novel application process designed specifically for medical professionals. With an emphasis on heart disease, our software helps physicians predict the onset or recurrence of non-communicable diseases (NCDs) by utilizing patient records. Thorough testing confirms the program's effectiveness and quick prediction skills, enabling well-informed choices on patient health risks. Our software prototype uses various risk characteristics to classify the risk of abrupt cardiac events in patients diagnosed with ischemic heart disease (IHD). This paper discusses different techniques that aim to raise awareness and facilitate preventive interventions to lower the occurrence of such events.

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Non-Communication Diseases (NCDs), Cardio vascular diseases(CVDs), Machine learning algorithms, Predictive solutions, Heart disease, Patient records, Risk analysis, Preventive interventions

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This pioneering project tackles the pressing issue of gas emission detection, crucial for environmental and human well-being. Conventional detection systems face limitations, prompting the exploration of hyperspectral image analysis for a safer and more efficient solution. Introducing a groundbreaking deep learning methodology for hyperspectral gas detection in the longwave infrared spectrum, this project merges unmixing and classification techniques. Through a specialized 3-D convolutional neural network and autoencoder-based network, it converts radiance data to luminance-temperature data, achieving remarkable performance surpassing conventional methods. Further innovation extends the approach with an Ensemble model, integrating CNN, Bi-directional, and GRU algorithms, enhancing input features for improved prediction accuracy. This innovative endeavor underscores the efficacy of modern techniques in addressing environmental challenges.

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Gas, 3D CNN

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Sensing is the main function of all autonomous driving and collects all the necessary information about the environment around the vehicle's movement. The decision making process uses the information needed to create strategies and make the right decision based on the situation to ensure the safety of passengers as much as possible. This article reviews recent literature on motor vehicle awareness (AVP), focusing on two main tasks: semantic segmentation and object detection. These two functions play an important role as an important part of the car navigation system. In the autonomous car, perception with point cloud semantic segmentation helps obtain a wealth of information about the surrounding road environment. Despite the massive progress of recent researches, the existing machine learning networks are still insufficient for online applications of autonomous driving due to too subdivided classes, the lack of training data, and their heavy computing load

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Local interpretation model-agnostic explanation,adaptive dehazing,metric learning

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The Mughal Empire, spanning from the 16th to the 19th centuries in the Indian subcontinent, witnessed the convergence of diverse culinary traditions, resulting in the rich and opulent Mughal cuisine. This abstract explores the history of food in the Mughal Empire, focusing on its culinary influences, agricultural practices, dining culture, and societal significance. Mughal cuisine, characterized by its fusion of Central Asian, Persian, and Indian culinary traditions, reflected the cosmopolitan nature of the empire. Agriculture played a crucial role in sustaining the empire's vast population, with sophisticated irrigation systems and crop cultivation techniques supporting the production of staple grains, fruits, and vegetables.

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Grains, cultivation techniques

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Kiruthavak kaappiyathil azhagiyal chinthanaikal

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Kiruthavak kaappiyathil azhagiyal chinthanaikal

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India is still a developing country and is considered as poor country having high unemployment rate and poverty. After new economic reforms in 1990, privatization has increased; there is decline in agricultural work force. Most of the population of Odisha lives in rural area and has high poverty and unemployment rate. Odisha is also known for its distress migration because of unavailability of jobs. In this context MGNREGA that provides guarantee wage employment has been studied based on secondary data in two selected panchayats of Balasore district to assess the employment opportunities and participation of vulnerable sections of the society. It was found that among the selected panchayats Anko has performed better.

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MGNREGA, Employment, Distress migration, Vulnerable sections

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Panhypopituitarism due to compression of pituitary gland by internal carotid artery branch is rare

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Panhypopituitarism, internal carotid artery branch, aneurysm, pituitary gland

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Radicular cysts are among the most common odontogenic cysts of the jaws. When they originate from primary teeth, nevertheless, they are rare. This article discusses the enucleation treatment for a 11-year-old boy who had a radicular cyst associated with his primary teeth.

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extraction, enucleation Odontogenic cyst, , primary teeth, radicular cyst

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In this project the attention is focused on developing a technique to straighten commercially used wire in an economical manner. There are machines developed by various industries to straighten drawn and coiled wire, but the commercial wires available in the market have differing cross section and material properties. Nowadays, straightening of these commercial wires are done manually in industries by hammering process. It is a very tedious job and time consuming too. So there is an industrial necessity to automate the process. Now days, there are wide variety of wire products like welding electrode, weld mesh, heat treated kitchen baskets, automobile spark plugs and exhaust valves and various applications etc. For manufacturing all above products, wire is used as primary element and is to be straighten from coil form. The straighten wire is to be cut as per required length. For these sequential operations, in wire straightening machine cutter can be used. Wire straightening machines play a pivotal role in straightening wires that have become bent or crooked during manufacturing or handling processes. The content outlines the working principle of these machines, highlighting their key components such as rollers or dies. It discusses the importance of wire straightening machines in industries such as construction, manufacturing, and automotive, where straightened wire is essential for various applications including fencing, reinforcement, and electrical wiring. Additionally, the paper emphasizes the efficiency and accuracy of these machines in improving product quality and production speed. Through a comprehensive analysis, this information provides insights into the critical role of wire straightening machines in modern industrial processes. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested to develop next generation of wire straightening machine.

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Unwinding , Straightening , Cutting off

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The present study aim to explores the extraction of natural colorants from Ixora and African tulip flowers. Natural pigments found in flowers offer a vibrant and ecofriendly alternative to synthetic dyes. Extracting these color involves separating the pigments from flower petals .The extracted color has the potential to serve as a sustainable and ecofriendly alternative to synthetic dyes. Both ixora and African tulip flower rich in antioxidant and anti-inflammatory compound. The project will investigate on various extraction methods and their optical properties. Natural pigments, including carotenoids, flavonoids and anthocyanidins, determine the attractive color of fruits. It also have health benefits that are seldom levarged by the food industry. Betalains, carotenoids, phycocyanins, and anthocyanins are major food colorants used in the food industry that have documented biological effects, particularly in the prevention and management of chronic diseases such as diabetes, obesity, and cardiovascular disease. In this experimental analysis measurement of the extracted color will be using colorimetry. For phytochemical analysis detection of alkaloids, terpenoids, phenolic compounds, flavonoids, tannins were tested. Antioxidant activity and total polyphenol were also noted

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Ixora Coccinea, Spathodea Campanulata, Extraction

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Artificial Intelligence (AI) programs are a common occurrence, and the use of deepfake software is steadily increasing. However, new security flaws and issues arise with every new piece of technology. Deepfakes are a new tool that the disinformation threat is using. There are obvious advantages to synthetic media or deepfakes in a number of fields, including criminal forensics, filmmaking, accessibility, education, and artistic expression. But as synthetic media technology becomes more widely available, so does the possibility of exploitation. Particularly women have suffered as a result of deepfake technology's advancement. They have become targets of revenge porn and have been harassed and bullied as a result. Deepfakes are videos that intentionally mislead people by using Artificial Intelligence (AI), deep learning, and photoshop to create images of real events that propagate false information. Deepfake technology is becoming more widely used, which puts women in India at serious risk and exacerbates already-existing issues with safety, privacy, and reputation. This article explores how deepfakes affect women, with particular attention to issues like misinformation, defamation, and the production of non-consensual pornography. Concerns regarding the need for more robust legal frameworks to address issues like privacy, data protection, and cybercrime have been raised by the growing use of fake technology. Although India has laws that can be used to counteract deepfake technology, more specialized legislation is required to address the particular problems that deepfakes present. The necessity for the Indian government to address this matter and create a regulatory framework is emphasized in this paper. Additionally, it examines how well the Indian legal system handles the violation of people's rights caused by deepfakes and makes the case that, even though the laws as they stand implicitly forbid the dissemination of deepfakes, there are certain issues with the system that could be resolved by proposed legislative changes.

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Artificial intelligence,Deepfake,Pornography,Criptography,Revenge porn

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The main objective of this paper is to analyze the historical background of women representation in Punjab Civil Services (Executive). The Secondary sources of information are used for this study. The entry of women in the civil services is a post - independence phenomenon. It was manned exclusively by men before 1947. After independence, the Indian constitution had permitted Indian women to enter the civil services. The present Punjab Civil Services came into existence on 1 November, 1966, when Punjab was reorganized on the basis of language. Although, the number of women in Punjab Civil Services has increased significantly but the number of women as compared to men, still on lower side. Since 1966, women's entry into Punjab Civil Services is not satisfactory and has been fluctuating from time to time. Female officers are still less in number as compared to male officers in Punjab Civil Services.

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Civil Services, Executive, Steel Frame, Administration, Women, Patriarchy

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Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e., larger than the image resolution. The receptive field refers to the set of input pixels that are path-connected to a neuron. For image inpainting task, however, the size of surrounding areas needed to repair different kinds of missing regions are different, and the very large receptive field is not always optimal, especially for the local structures and textures. In addition, a large receptive field tends to involve more undesired completion results, which will disturb the inpainting process. Based on these, a novel three-stage inpainting framework with local and global refinement is proposed.

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Image inpainting, neural networks, receptive field

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Distributed Generation (DG) is a new source that is used in sales structures. The directorates-general get involved both with the help of switchgear operators and indirectly with the help of customers. Customer load centers. Excessive use of DG leads to the development of the resistor quality, the development of the voltage profile and lower losses. Due to the growing desire for the release of resistance agents in resistance structures, their problems must be investigated. The main problems facing the distributed armed forces are unplanned islands. Unplanned islands pose some risks to resistance structures and the mechanic working with the wrong equipment. The proposed technique is mainly based on the wavelet revision and a new classifier called Neural Artificial. Network (ANN) and Support Vector Machine (SVM): The proposed technology is used in an IEEE 14 bus network in MATLAB / SIMULINK software. Island detection precision of the proposed technique. In this system, it is assumed that the generator as a wind turbine is an issued resource. Finally, the ANN and SVM ratings are compared and finally the final offer for the fine classifier for island detection for the distributed generator device is prepared.

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: Microgrid System, Matlab/Simulink

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We use an algorithm for unsupervised detection of anomalous regions in video sequences, termed "KMEANS," which identifies coherent spatial regions and time intervals indicative of anomalies. In contrast to existing techniques focusing on isolated anomalous data points, our approach enables ranking regions of varying sizes and ensures scalability for large-scale datasets through an interval proposal technique. Experimental validation on synthetic and real data spanning domains such as climate analysis, video surveillance, and text forensics underscores the broad applicability and utility of our method in uncovering noteworthy events across diverse data types.

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Anomaly detection, video sequences, KMeans, unsupervised learning, spatial coherence, time intervals, scalability, synthetic data, real-world applications, event detection.

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Relevance: Job Satisfaction is the height to which an individual is content with his/her job, considering various aspects such as work environment, tasks, compensation, relationships with colleagues, opportunities for advancement, and alignment with personal values and goals. It directly affects the performance of an employee in turn impacting the growth of the organization. All organizations need to take care of the satisfaction level of their employees for their better growth. Purpose: This paper represents a systematic literature review of the Job satisfaction of secondary school teachers. As we know Satisfied teachers perform well and give their full effort toward their responsibilities to the students and society, it will give a clear picture of the factors contributing to the teacher's satisfaction level. Method: Descriptive research was conducted using a qualitative technique, that includes a systematic literature review. Searches on Research Gate, Google Scholar, and ScienceDirect focused on secondary school teachers' job satisfaction were taken into consideration. The review comprised a few studies completed between 2014 and 2024. Findings: Altogether 140 titles were picked out, and 19 of them satisfied the requirements/criteria for discussing secondary school teachers' work satisfaction. Conclusion: Secondary school teachers' Job happiness is a vital aspect of all educational system's effectiveness and the all-around well-being of educators. Teachers play a fundamental role in shaping the future by imparting knowledge and nurturing the next generation. Satisfied teachers are more motivated, engaged, and effective in their job roles, leading to better student outcomes and a positive learning environment.

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Job Satisfaction, School teachers, Job performance management

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Background: Premenstrual syndrome (PMS) encompasses a range of physical, psychological, emotional, and mood changes that typically occur following ovulation in women. Globally, approximately 47.8% of girls in the reproductive age group experience PMS, with the highest occurrence observed in developed nations.1 Aims and objectives: The aim of this study was to assess the level of knowledge regarding PMS among adolescent girls and also to determine the association of knowledge with selected demographic variables among adolescent girls at selected schools, Punjab, India. Materials and Methods: A quantitative approach employing a descriptive research design was utilized in this study. A sample of 180 adolescent girls was selected based on specific inclusion and exclusion criteria, employing a nonprobability purposive sampling technique from selected schools in Punjab. Data was collected using a structured knowledge questionnaire. The obtained data was then tabulated and analyzed according to the study's objectives using descriptive and inferential statistics. Results: Findings revealed that majority of the adolescent girls (71.11%) had poor and (17.78%) had average knowledge level regarding PMS. A smaller portion of 11.11% demonstrated good knowledge, while none of the participants achieved an excellent level of knowledge regarding PMS. Overall adolescent girls were having average mean knowledge score. There was significant association of level of knowledge of adolescent girls regarding PMS with age and family history of PMS as statistically analyzed by ?2 value (25.76, P<0.05) and (32.53, P<0.05) respectively at 0.05 level of significance among adolescent girls.

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Premenstrual Syndrome, knowledge, adolescent girls, schools

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The right to education is a fundamental human right that is recognized and enshrined in various international human rights treaties and agreements. In India, the right to education has been enshrined as a fundamental right for children under the age of 14 through the 86th Amendment to the Constitution in 2002. Objective of the research is that the right to education enshrined for children in India. The research method followed here by the researcher is empirical research. The total number of samples is 200. From 200 people the researcher has got the data.Convenient sampling method is used to collect the samples. Major findings of the research is that the right to education is a fundamental right. The state shall provide free and compulsory education to all children of 6 to 14 years. The importance of education in the lives of the children at present. Implementing laws compelling in the state. The quality of free education is good provided to the children. There are many children who are beneficiaries to this scheme of the state. The fact that the private unaided schools had 25% of the total strength of the RTE quota. Every child in need of education does not have access to free and compulsory education. The major barriers that prevent children from realising their right to education are poverty, inadequate infrastructure, discrimination, cultural attitudes and disability. The measures taken by the government for the education of the children are satisfied. The enshrinement of the right to education as a fundamental right for children in India is a crucial step towards ensuring that every child has access to quality education. The aim of the research is to study the right to education enshrined for children in India.

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Education, Children, Discrimination, Quality Education and Fundamental Right.

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Mouth ulcers are a common, painful condition that can have a detrimental effect on one's quality of life. The creation of novel treatments derived from natural sources, such as plant extracts and phytochemicals, is gaining popularity. One such plant that has shown promise for therapeutic effects but has not received enough attention is Acmella ulignosa. The current status of research on Acmella ulignosa is outlined in this review, along with an assessment of the plant's potential for use as an integrative medicine to treat mouth ulcers. The goal of future studies should be to clarify the pharmacological processes by which products derived from Acmella ulignosa provide anti-mouth ulcer therapeutic benefits. In summary, Acmella ulignosa exhibits early promise as a novel therapeutic agent for a prevalent chronic illness that requires enhanced integrative medical solutions with negligible side effects.

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Mouth ulcer, Acmella ulignosa, Extraction, TLC, Photoluminance study

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