Volume 13 | Issue 4 |

 Abstract

To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. We analyze this pattern by taking crime datasets from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. This dataset includes different blocks of the city of Chicago. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in Chicago. Finally, this paper uses a different algorithm like Random Forest, Decision Tree and different ensemble methods such as Extra Trees, Bagging and AdaBoost to evaluate the accuracy given by each algorithm

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 Keywords

distance-decay principle, space tendency, transition density mode

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The integration of Artificial Intelligence (AI) into corporate governance is reshaping how organizations manage accountability, transparency, and decision-making. AI technologies offer powerful tools for enhancing risk assessment, regulatory compliance, fraud detection, and board-level decision support. This paper explores the transformative role of AI in strengthening corporate governance structures, examining both the opportunities and ethical challenges that arise from AI adoption. By analyzing current use cases, regulatory implications, and governance frameworks, the study provides insights into how businesses can responsibly harness AI to drive sustainable and ethical corporate practices. The discussion also highlights the need for robust oversight mechanisms to ensure AI systems align with governance principles and stakeholder expectations.

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 Keywords

Artificial Intelligence (AI), Corporate Governance, Ethical AI, Risk Management, Transparency, Accountability, Decision-Making, Board of Directors, Compliance, Regulatory Technology (RegTech), Fraud Detection, Data Governance, AI Oversight, Algorithmic Accountability, Governance Frameworks, Stakeholder Trust, AI Ethics, Automation in Governance, Predictive Analytics, Responsible AI.

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Multicast is used in networking to efficiently send data from one sender to multiple receivers, without burdening the network with multiple copies of the same data. It helps in efficient usage of bandwidth Instead of sending individual copies of the same data to each receiver (as with unicast), multicast sends a single copy of the data to all receivers that are interested. This saves bandwidth, especially when there are many receivers. Sending a video stream to multiple devices can be done using broadcast, but a broadcast packet sends traffic to all ports and will not be forwarded across routers. To overcome this limitation, we use multicast. This paper provides a brief overview of the components of multicast and the protocols that help multicast to work

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 Keywords

IGMP,PIM,PIM Sparse, PIM Dense, Multicasting

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Fraudulent job ads have grown to be a major problem in online recruiting due to the quick expansion of online employment platforms. We suggest an improved job categorisation system that uses cutting-edge deep learning algorithms to identify fake job ads in order to address this issue. We use a Convolutional Neural Network (CNN2D) to enhance feature extraction and maximise classification performance, building on the usage of transformer-based models such as BERT and RoBERTa. By employing 2D convolutional layers, the CNN2D model efficiently and accurately detects fraudulent job ads by capturing intricate patterns in the dataset. Furthermore, the system's user-friendly interface, which makes use of the Flask framework, allows for easy job posting administration and real-time fraud detection. This enhancement increases the accuracy and expediency of the process of identifying fraudulent job postings.

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 Keywords

Fraudulent Job Postings, Convolutional Neural Network (Cnn2d), Feature Extraction, Real-Time Fraud Detection.

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Early detection and classification of cataracts are crucial for timely medical intervention and vision preservation. Traditional diagnostic methods, such as slit-lamp examinations and fundus imaging, rely heavily on ophthalmologists' expertise, making the process subjective, time-consuming, and resource- intensive. To address these limitations, deep learning-based techniques have emerged as powerful tools for automated cataract detection. In this study, we explore the use of the Inception V3 convolutional neural network (CNN) classifier for cataract detection from fundus images. Inception V3, known for its multi-scale feature extraction capabilities, optimized architecture, and high accuracy in image classification tasks, is trained on a dataset of labeled cataract images. The model undergoes transfer learning and fine-tuning to improve performance on medical image datasets. Experimental results demonstrate that Inception V3 achieves high classification accuracy, outperforming traditional machine learning approaches and other CNN architectures. Furthermore, we analyze the effectiveness of ensemble learning, hybrid CNN- LSTM models, and transfer learning techniques to enhance model robustness and generalizability. The integration of transfer learning, ensemble learning, and hybrid architectures has further improved the effectiveness of cataract classification. While challenges remain in terms of data quality, interpretability, and deployment, ongoing advancements in AI and cloud computing hold the potential to make automated cataract screening accessible globally. The study also highlights challenges such as dataset limitations, image quality variations, and the need for explainable AI in medical applications. Future research directions include the integration of attention mechanisms, real-time deployment in mobile screening applications, and collaboration with clinical experts to refine AI- driven diagnostic systems

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 Keywords

Cataract Detection, Inception V3, Convolution Neural Network, DeepLearning

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This research explores the effect of social media on investment decisions in the stock market, with specific reference to the influence of Facebook and Instagram on investor behavior. Investors have become more dependent on social media, as it has become a go-to source of real-time data, market sentiment, and trends. The research draws on data from surveys undertaken with different respondents to gain a better insight into the connection between social media usage and investment behavior. Implications of the findings are expected to shed light on the influence of social media on investors' decision-making process, specifically regarding risk perception, trust, and the tempo of investment behavior. Through the examination of these dynamics, the study hopes to inform policymakers, educators, and investors on how to best leverage social media to create well-informed and responsible investing behavior. Overall, this study illustrates the increasing importance of digital communication in financial markets and how it may increase and complicate the investment environment.

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 Keywords

Social Media, Stock Market, Investor Behavior, Financial Decision-Making, Online Platforms.

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The old age is an integral part of human life. It is the evening of life. It is unavoidable, undesirable, unwelcome and problem ridden phase of life. But it is really interesting to note that everybody likes to live a long life, but not to be old. It is ironical that however undesirable the old age that is bound to life. Reminiscence therapy for depression consists of recollecting of past life experiences which implicates the principles of cognitive therapy. However there are inherent difficulties in assuming from these studies that reminiscence therapy for depression is an effective treatment with the general adult population. The research design adopted for the study was quasi experimental non-equivalent control group design. The study was conducted at selected Geriatric home, Madurai. The samples selected were 60, of which 30 samples were allotted in the experimental group and the remaining 30 were in the control group. The samples were recognized based on the inclusion criteria and picked up with Non probability - purposive sampling technique. Yesavage geriatric depression scale was used as a tool for data collection after confirming the validity and reliability. Reminiscence therapy was demonstrated to the experimental group only for 21 days in both morning and evening. The data obtained were analysed and interpreted using descriptive and inferential statistics. The comparison of mean post test score of level of depression among geriatrics between experimental and control group using unpaired 't' test revealed that the 't' value was 22.25 which showed a statistical significance at p<0.001 level. It was inferred that the reminiscence therapy was significantly effective to decrease the level of depression among elderly in experimental group. The chi square test revealed that there was significant association between pretest level of depression among elderly with the selected demographic variables like gender(p=0.0102), marital status(p=0.0051) and any medical illness (p=0.0012) in experimental group and control group and remaining was not associated with demographic variables at p < 0.05 level.

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Reminiscence therapy, Effectiveness, Depression, Elderly, Old-age home

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This research paper explores the historical institution of matriliny among the Nairs of the Malabar region in Kerala, India. The Nair Tharavad (matrilineal joint family) system stands as a unique socio-cultural structure that redefined gender roles, property rights, and power dynamics within a patriarchal Indian context. This study examines the origins, development, and eventual decline of the system, offering insights into how gender and power were negotiated and performed in a matrilineal framework. Through archival sources, colonial records, and oral histories, the paper critically analyses the intersection of gender and kinship and its implications for contemporary debates on family, inheritance, and women's agency.

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 Keywords

Nair, Tharavad, Matriliny, Gender, Power, Malabar, Kinship, Inheritance, Colonialism, Marumakkathayam

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Numerous multipurpose perovskite materials have been prepared in recent years as a result of the remarkable versatility of perovskite structure in accommodating various dopant ions. By doping divalent Zr4+ ions into the octahedral positions of lead zirconate-titanate perovskite, we hope to create stable and extremely high dielectric constant lead-based materials for supercapacitors. Due to its intriguing ferroelectric, dielectric, piezoelectric, and other electrical properties, lead titanate based materials have attracted a lot of attention from material scientists.Here in this study, we report synthesis of PbZr0.01Ti0.99O3 material by high temperature solid synthesis technique. The Rietveld refinement of the sample confirms the pure phase of tetragonal structure with space group of P4mm.William son-Hall analysis of the XRD data revels the size of crystallite to be about 50nm and the strain arising from the high firing and intense grinding effect to be 8.54*10-3. The electrical impedance and electrical modulus as a function of ac field was investigated to emphasize on the conduction mechanism in the sample.

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 Keywords

Crystal Structure, Rietveld Refinement, Williamson-Hall Method, Conduction Mechanism.

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Climate change is no longer just about the environment, it has become a serious threat to global peace and security. This article examines how increased temperatures, natural disasters, and loss of resources such as water and land can result in conflict, migration, and instability. Climate change aggravates existing issues in the world, particularly among nations that are already politically unstable, or are facing other challenges. Through the use of actual examples in regions such as the Sahel region, Lake Chad, and Syria, this article shows how environment related problems may result in violence and unrest in the global arena. The article also discusses the way international organizations like the United Nations, African Union, and European Union are starting to think of climate change as a matter of security. Treaties like the Paris Agreement and steps taken by certain nations are instances of this increasing consciousness. Furthermore, this article is particularly concerned with how these issues impact the Global South the most. It demands global action to lower climate threats, promote peace-building, and sustain development. To address these issues, we need to consider climate security as a vital component of international peace efforts.

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 Keywords

climate change, environmental degradation, international security, migration, UN, Global South, security threat

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